Medical coding system with integrated codebook interface

ABSTRACT

Techniques for use in medical coding include applying a natural language understanding engine to a free-form text documenting at least one clinical patient encounter to generate a set of one or more medical billing codes for the patient encounter. A user interface may be provided, configured to allow one or more human users to review and correct the generated set of medical billing codes. Within the user interface, in response to user selection of a first medical billing code of the generated set of medical billing codes, at least a portion of a government-authorized codebook for the first medical billing code may be displayed, and a position of the first medical billing code may be indicated in the displayed portion of the codebook.

BACKGROUND

Medical documentation is an important process in the healthcareindustry. Most healthcare institutions maintain a longitudinal medicalrecord (e.g., spanning multiple observations or treatments over time)for each of their patients, documenting, for example, the patient'shistory, encounters with clinical staff within the institution,treatment received, and/or plans for future treatment. Suchdocumentation facilitates maintaining continuity of care for the patientacross multiple encounters with various clinicians over time. Inaddition, when an institution's medical records for large numbers ofpatients are considered in the aggregate, the information containedtherein can be useful for educating clinicians as to treatment efficacyand best practices, for internal auditing within the institution, forquality assurance, etc.

Historically, each patient's medical record was maintained as a physicalpaper folder, often referred to as a “medical chart”, or “chart”. Eachpatient's chart would include a stack of paper reports, such as intakeforms, history and immunization records, laboratory results andclinicians' notes. Following an encounter with the patient, such as anoffice visit, a hospital round or a surgical procedure, the clinicianconducting the encounter would provide a narrative note about theencounter to be included in the patient's chart. Such a note couldinclude, for example, a description of the reason(s) for the patientencounter, an account of any vital signs, test results and/or otherclinical data collected during the encounter, one or more diagnosesdetermined by the clinician from the encounter, and a description of aplan for further treatment. Often, the clinician would verbally dictatethe note into an audio recording device or a telephone giving access tosuch a recording device, to spare the clinician the time it would taketo prepare the note in written form. Later, a medical transcriptionistwould listen to the audio recording and transcribe it into a textdocument, which would be inserted on a piece of paper into the patient'schart for later reference.

Currently, many healthcare institutions are transitioning or havetransitioned from paper documentation to electronic medical recordsystems, in which patients' longitudinal medical information is storedin a data repository in electronic form. Besides the significantphysical space savings afforded by the replacement of paperrecord-keeping with electronic storage methods, the use of electronicmedical records also provides beneficial time savings and otheropportunities to clinicians and other healthcare personnel. For example,when updating a patient's electronic medical record to reflect a currentpatient encounter, a clinician need only document the new informationobtained from the encounter, and need not spend time entering unchangedinformation such as the patient's age, gender, medical history, etc.Electronic medical records can also be shared, accessed and updated bymultiple different personnel from local and remote locations throughsuitable user interfaces and network connections, eliminating the needto retrieve and deliver paper files from a crowded file room.

Another modern trend in healthcare management is the importance ofmedical coding for documentation and billing purposes. In the medicalcoding process, documented information regarding a patient encounter,such as the patient's diagnoses and clinical procedures performed, isclassified according to one or more standardized sets of codes forreporting to various entities such as payment providers (e.g., healthinsurance companies that reimburse clinicians for their services). Inthe United States, some such standardized code systems have been adoptedby the federal government, which then maintains the code sets andrecommends or mandates their use for billing under programs such asMedicare.

For example, the International Classification of Diseases (ICD)numerical coding standard, developed from a European standard by theWorld Health Organization (WHO), was adopted in the U.S. in versionICD-9-CM (Clinically Modified). It is mandated by the Health InsurancePortability and Accountability Act of 1996 (HIPAA) for use in codingpatient diagnoses. The Centers for Disease Control (CDC), the NationalCenter for Health Statistics (NCHS), and the Centers for Medicare andMedicaid Services (CMS) are the U.S. government agencies responsible foroverseeing all changes and modifications to ICD-9-CM, and a new versionICD-10-CM is scheduled for adoption in 2015.

Another example of a standardized code system adopted by the U.S.government is the Current Procedural Terminology (CPT) code set, whichclassifies clinical procedures in five-character alphanumeric codes. TheCPT code set is owned by the American Medical Association (AMA), and itsuse is mandated by CMS as part of the Healthcare Common Procedure CodingSystem (HCPCS). CPT forms HCPCS Level I, and HCPCS Level II adds codesfor medical supplies, durable medical goods, non-physician healthcareservices, and other healthcare services not represented in CPT. CMSmaintains and distributes the HCPCS Level II codes with quarterlyupdates.

Conventionally, the coding of a patient encounter has been a manualprocess performed by a human professional, referred to as a “medicalcoder” or simply “coder,” with expert training in medical terminologyand documentation as well as the standardized code sets being used andthe relevant regulations. The coder would read the availabledocumentation from the patient encounter, such as the clinicians'narrative reports, laboratory and radiology test results, etc., anddetermine the appropriate codes to assign to the encounter. The codermight make use of a medical coding system, such as a software programrunning on suitable hardware, that would display the documents from thepatient encounter for the coder to read, and allow the coder to manuallyinput the appropriate codes into a set of fields for entry in therecord. Once finalized, the set of codes entered for the patientencounter could then be sent to a payment provider, which wouldtypically determine the level of reimbursement for the encounteraccording to the particular codes that were entered.

SUMMARY

One type of embodiment is directed to a method comprising: applying anatural language understanding engine to a free-form text documenting atleast one clinical patient encounter to generate a set of one or moremedical billing codes for the at least one clinical patient encounter;providing a user interface configured to allow one or more human usersto review and correct the generated set of medical billing codes; andwithin the user interface, in response to user selection of a firstmedical billing code of the generated set of medical billing codes,displaying at least a portion of a government-authorized codebook forthe first medical billing code, and indicating a position of the firstmedical billing code in the displayed at least a portion of thecodebook.

Another type of embodiment is directed to at least one computer-readablestorage medium storing computer-executable instructions that, whenexecuted, perform a method comprising: applying a natural languageunderstanding engine to a free-form text documenting at least oneclinical patient encounter to generate a set of one or more medicalbilling codes for the at least one clinical patient encounter; providinga user interface configured to allow one or more human users to reviewand correct the generated set of medical billing codes; and within theuser interface, in response to user selection of a first medical billingcode of the generated set of medical billing codes, displaying at leasta portion of a government-authorized codebook for the first medicalbilling code, and indicating a position of the first medical billingcode in the displayed at least a portion of the codebook.

Another type of embodiment is directed to apparatus comprising at leastone processor, and at least one storage medium storingprocessor-executable instructions that, when executed by the at leastone processor, perform a method comprising: applying a natural languageunderstanding engine to a free-form text documenting at least oneclinical patient encounter to generate a set of one or more medicalbilling codes for the at least one clinical patient encounter; providinga user interface configured to allow one or more human users to reviewand correct the generated set of medical billing codes; and within theuser interface, in response to user selection of a first medical billingcode of the generated set of medical billing codes, displaying at leasta portion of a government-authorized codebook for the first medicalbilling code, and indicating a position of the first medical billingcode in the displayed at least a portion of the codebook.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a block diagram of an exemplary operating environment for aclinical language understanding (CLU) system that may be employed inconnection with some embodiments;

FIG. 2 is a screenshot illustrating an exemplary graphical userinterface for review of extracted medical facts in accordance with someembodiments;

FIGS. 3A and 3B are screenshots illustrating an exemplary display ofmedical facts in a user interface in accordance with some embodiments;

FIG. 4 is a screenshot illustrating an exemplary display of linkagebetween text and a medical fact in accordance with some embodiments;

FIG. 5 is a screenshot illustrating an exemplary interface for enteringa medical fact in accordance with some embodiments;

FIG. 6 is a block diagram of an exemplary computer system on whichaspects of some embodiments may be implemented;

FIGS. 7A-7G are screenshots illustrating an exemplary user interface fora computer-assisted coding (CAC) system in accordance with someembodiments;

FIG. 8 is a screenshot illustrating an exemplary code finalizationscreen in accordance with some embodiments; and

FIG. 9 is a block diagram of an exemplary computer system on whichaspects of some embodiments may be implemented;

DETAILED DESCRIPTION

Some embodiments described herein may make use of a natural languageunderstanding (NLU) engine to automatically derive medical billing codesfor a clinical patient encounter from free-form text documenting theencounter. In some embodiments, the NLU engine may be implemented aspart of a clinical language understanding (CLU) system; examples ofpossible functionality for such a CLU system are described in detailbelow. In some embodiments, the medical billing codes derived by the NLUengine may be suggested to a human user such as a medical codingprofessional (“coder”) coding the patient encounter via acomputer-assisted coding (CAC) system; examples of possiblefunctionality for such a CAC system are also described in detail below.

In some embodiments, as described below, the CAC system may provide auser interface configured to allow the human coder to review and correctthe medical billing codes automatically derived and suggested by the NLUengine. Further, in some embodiments, the CAC interface may provide thecoder with interactive access to a knowledge base of coding guidelines,rules, decision support, etc., to aid the coder in accurately andefficiently reviewing suggested codes and coding the encounter. Forexample, in some embodiments, in response to user selection of one ofthe automatically suggested codes in the CAC interface, agovernment-authorized codebook for the code set of the suggested code(or a portion thereof) may be displayed within the user interface. Forexample, for a suggested ICD-9-CM code, the official ICD-9-CM codebookauthorized by one or more U.S. government agencies may be displayed opento the selected code. For a suggested HCPCS code, the official HCPCScodebook authorized by CMS may be displayed open to the selected code.

In some embodiments, the position of the selected code within thedisplayed codebook may be indicated, and the interface may enable touser to navigate the codebook from that location to explore relatedcodes. Descriptions, rules, guidelines, tips, and/or other usefulinformation may also be accessed within the user interface throughinteraction with the displayed codebook. In some embodiments, suchfeatures may advantageously allow a coder to efficiently research codesthat have been suggested for consideration for a patient encounter, andmay allow the coder to conveniently explore related codes for exposureto potential changes that may improve the accuracy of the codessubmitted for the patient encounter.

While a number of inventive features for clinical documentationprocesses are described above, it should be appreciated that embodimentsof the present invention may include any one of these features, anycombination of two or more features, or all of the features, as aspectsof the invention are not limited to any particular number or combinationof the above-described features. The aspects of the present inventiondescribed herein can be implemented in any of numerous ways, and are notlimited to any particular implementation techniques. Described below areexamples of specific implementation techniques; however, it should beappreciate that these examples are provided merely for purposes ofillustration, and that other implementations are possible.

Clinical Language Understanding (CLU) System

An Electronic Health Record (EHR) is an electronic medical record thatgenerally is maintained by a specific healthcare institution andcontains data documenting the care that a specific patient has receivedfrom that institution over time. Typically, an EHR is maintained as astructured data representation, such as a database with structuredfields. Each piece of information stored in such an EHR is typicallyrepresented as a discrete (e.g., separate) data item occupying a fieldof the EHR database. For example, a 55-year old male patient named JohnDoe may have an EHR database record with “John Doe” stored in thepatient_name field, “55” stored in the patient_age field, and “Male”stored in the patient_gender field. Data items or fields in such an EHRare structured in the sense that only a certain limited set of validinputs is allowed for each field. For example, the patient_name fieldmay require an alphabetic string as input, and may have a maximum lengthlimit; the patient_age field may require a string of three numerals, andthe leading numeral may have to be “0” or “1”; the patient_gender fieldmay only allow one of two inputs, “Male” and “Female”; apatient_birth_date field may require input in a “MM/DD/YYYY” format;etc.

Typical EHRs are also structured in terms of the vocabulary they use, asmedical terms are normalized to a standard set of terms utilized by theinstitution maintaining the EHR. The standard set of terms may bespecific to the institution, or may be a more widely used standard. Forexample, a clinician dictating or writing a free-form note may use anyof a number of different terms for the condition of a patient currentlysuffering from an interruption of blood supply to the heart, including“heart attack”, “acute myocardial infarction”, “acute MI” and “AMI”. Tofacilitate interoperability of EHR data between various departments andusers in the institution, and/or to allow identical conditions to beidentified as such across patient records for data analysis, a typicalEHR may use only one standardized term to represent each individualmedical concept. For example, “acute myocardial infarction” may be thestandard term stored in the EHR for every case of a heart attackoccurring at the time of a clinical encounter. Some EHRs may representmedical terms in a data format corresponding to a coding standard, suchas the International Classification of Disease (ICD) standard. Forexample, “acute myocardial infarction” may be represented in an EHR as“ICD-9 410”, where 410 is the code number for “acute myocardialinfarction” according to the ninth edition of the ICD standard.

To allow clinicians and other healthcare personnel to enter medicaldocumentation data directly into an EHR in its discrete structured dataformat, many EHRs are accessed through user interfaces that makeextensive use of point-and-click input methods. While some data items,such as the patient's name, may require input in (structured) textual ornumeric form, many data items can be input simply through the use of amouse or other pointing input device (e.g., a touch screen) to makeselections from pre-set options in drop-down menus and/or sets ofcheckboxes and/or radio buttons or the like.

While some clinicians may appreciate the ability to directly enterstructured data into an EHR through a point-and-click interface, manyclinicians may prefer being unconstrained in what they can say and inwhat terms they can use in a free-form note, and many may be reluctantto take the time to learn where all the boxes and buttons are and whatthey all mean in an EHR user interface. In addition, many clinicians mayprefer to take advantage of the time savings that can be gained byproviding notes through verbal dictation, as speech can often be afaster form of data communication than typing or clicking through forms.

Accordingly, some embodiments described herein relate to techniques forenhancing the creation and use of structured electronic medical records,using techniques that enable a clinician to provide input andobservations via a free-form narrative clinician's note. Someembodiments involve the automatic extraction of discrete medical facts(e.g., clinical facts), such as could be stored as discrete structureddata items in an electronic medical record, from a clinician's free-formnarration of a patient encounter. In this manner, free-form input may beprovided, but the advantages of storage, maintenance and accessing ofmedical documentation data in electronic forms may be maintained. Forexample, the storage of a patient's medical documentation data as acollection of discrete structured data items may provide the benefits ofbeing able to query for individual data items of interest, and beingable to assemble arbitrary subsets of the patient's data items into newreports, orders, invoices, etc., in an automated and efficient manner.

Automatic extraction of medical facts (e.g., clinical facts) from afree-form narration may be performed in any suitable way using anysuitable technique(s), as aspects of the present invention are notlimited in this respect. In some embodiments, pre-processing may beperformed on a free-form narration prior to performing automatic factextraction, to determine the sequence of words represented by thefree-form narration. Such pre-processing may also be performed in anysuitable way using any suitable technique(s), as aspects of the presentinvention are not limited in this respect. For example, in someembodiments, the clinician may provide the free-form narration directlyin textual form (e.g., using a keyboard or other text entry device), andthe textual free-form narration may be automatically parsed to determineits sequence of words. In other embodiments, the clinician may providethe free-form narration in audio form as a spoken dictation, and anaudio recording of the clinician's spoken dictation may be receivedand/or stored. The audio input may be processed in any suitable wayprior to or in the process of performing fact extraction, as aspects ofthe invention are not limited in this respect. In some embodiments, theaudio input may be processed to form a textual representation, and factextraction may be performed on the textual representation. Suchprocessing to produce a textual representation may be performed in anysuitable way. For example, in some embodiments, the audio recording maybe transcribed by a human transcriptionist, while in other embodiments,automatic speech recognition (ASR) may be performed on the audiorecording to obtain a textual representation of the free-form narrationprovided via the clinician's dictation. Any suitable automatic speechrecognition technique may be used, as aspects of the present inventionare not limited in this respect. In other embodiments, speech-to-textconversion of the clinician's audio dictation may not be required, as atechnique that does not involve processing the audio to produce atextual representation may be used to determine what was spoken. In oneexample, the sequence of words that was spoken may be determineddirectly from the audio recording, e.g., by comparing the audiorecording to stored waveform templates to determine the sequence ofwords. In other examples, the clinician's speech may not be recognizedas words, but may be recognized in another form such as a sequence orcollection of abstract concepts. It should be appreciated that the wordsand/or concepts represented in the clinician's free-form narration maybe represented and/or stored as data in any suitable form, includingforms other than a textual representation, as aspects of the presentinvention are not limited in this respect.

In some embodiments, one or more medical facts (e.g., clinical facts)may be automatically extracted from the free-form narration (in audio ortextual form) or from a pre-processed data representation of thefree-form narration using a fact extraction component applying naturallanguage understanding techniques, such as a natural languageunderstanding (NLU) engine. In some embodiments, the medical facts to beextracted may be defined by a set of fact categories (also referred toherein as “fact types” or “entity types”) commonly used by clinicians indocumenting patient encounters. In some embodiments, a suitable set offact categories may be defined by any of various known healthcarestandards. For example, in some embodiments, the medical facts to beextracted may include facts that are required to be documented byMeaningful Use standards promulgated by the U.S. government, e.g., under42 C.F.R. § 495, which sets forth “Objectives” specifying items ofmedical information to be recorded for medical patients. Such factscurrently required by the Meaningful Use standards include socialhistory facts, allergy facts, diagnostic test result facts, medicationfacts, problem facts, procedure facts, and vital sign facts. However,these are merely exemplary, as aspects of the invention are not limitedto any particular set of fact categories. Some embodiments may not useone or more of the above-listed fact categories, and some embodimentsmay use any other suitable fact categories. Other non-limiting examplesof suitable categories of medical facts include findings, disorders,body sites, medical devices, subdivided categories such as observablefindings and measurable findings, etc. The fact extraction component maybe implemented in any suitable form, as aspects of the present inventionare not limited in this respect. Exemplary implementations for a factextraction component are described in detail below.

Some embodiments described herein may make use of a clinical languageunderstanding (CLU) system, an exemplary operating environment for whichis illustrated in FIG. 1. CLU system 100, illustrated in FIG. 1, may beimplemented in any suitable form, as aspects of the present inventionare not limited in this respect. For example, system 100 may beimplemented as a single stand-alone machine, or may be implemented bymultiple distributed machines that share processing tasks in anysuitable manner. System 100 may be implemented as one or more computers;an example of a suitable computer is described below. In someembodiments, system 100 may include one or more tangible, non-transitorycomputer-readable storage devices storing processor-executableinstructions, and one or more processors that execute theprocessor-executable instructions to perform the functions describedherein. The storage devices may be implemented as computer-readablestorage media encoded with the processor-executable instructions;examples of suitable computer-readable storage media are discussedbelow.

As depicted, exemplary system 100 includes an ASR engine 102, a factextraction component 104, and a fact review component 106. Each of theseprocessing components of system 100 may be implemented in software,hardware, or a combination of software and hardware. Componentsimplemented in software may comprise sets of processor-executableinstructions that may be executed by the one or more processors ofsystem 100 to perform the functionality described herein. Each of ASRengine 102, fact extraction component 104 and fact review component 106may be implemented as a separate component of system 100, or anycombination of these components may be integrated into a singlecomponent or a set of distributed components. In addition, any one ofASR engine 102, fact extraction component 104 and fact review component106 may be implemented as a set of multiple software and/or hardwarecomponents. It should be understood that any such component depicted inFIG. 1 is not limited to any particular software and/or hardwareimplementation and/or configuration. Also, not all components ofexemplary system 100 illustrated in FIG. 1 are required in allembodiments. For example, in some embodiments, a CLU system may includefunctionality of fact extraction component 104, which may be implementedusing a natural language understanding (NLU) engine, without includingASR engine 102 and/or fact review component 106.

As illustrated in FIG. 1, user interface 110 is presented to a clinician120, who may be a physician, a physician's aide, a nurse, or any otherpersonnel involved in the evaluation and/or treatment of a patient 122in a clinical setting. During the course of a clinical encounter withpatient 122, or at some point thereafter, clinician 120 may wish todocument the patient encounter. Such a patient encounter may include anyinteraction between clinician 120 and patient 122 in a clinicalevaluation and/or treatment setting, including, but not limited to, anoffice visit, an interaction during hospital rounds, an outpatient orinpatient procedure (surgical or non-surgical), a follow-up evaluation,a visit for laboratory or radiology testing, etc. One method thatclinician 120 may use to document the patient encounter may be to entermedical facts that can be ascertained from the patient encounter intouser interface 110 as discrete structured data items. The set of medicalfacts, once entered, may be transmitted in some embodiments via anysuitable communication medium or media (e.g., local and/or networkconnection(s) that may include wired and/or wireless connection(s)) tosystem 100. Specifically, in some embodiments, the set of medical factsmay be received at system 100 by a fact review component 106, exemplaryfunctions of which are described below.

Another method that may be used by clinician 120 to document the patientencounter is to provide a free-form narration of the patient encounter.In some embodiments, the narration may be free-form in the sense thatclinician 120 may be unconstrained with regard to the structure andcontent of the narration, and may be free to provide any sequence ofwords, sentences, paragraphs, sections, etc., that he would like. Insome embodiments, there may be no limitation on the length of thefree-form narration, or the length may be limited only by the processingcapabilities of the user interface into which it is entered or of thelater processing components that will operate upon it. In otherembodiments, the free-form narration may be constrained in length (e.g.,limited to a particular number of characters).

A free-form narration of the patient encounter may be provided byclinician 120 in any of various ways. One way may be to manually enterthe free-form narration in textual form into user interface 110, e.g.,using a keyboard. In this respect, the one or more processors of system100 and/or of a client device in communication with system 100 may insome embodiments be programmed to present a user interface including atext editor/word processor to clinician 120. Such a text editor/wordprocessor may be implemented in any suitable way, as aspects of thepresent invention are not limited in this respect.

Another way to provide a free-form narration of the patient encountermay be to verbally speak a dictation of the patient encounter. Such aspoken dictation may be provided in any suitable way, as aspects of thepresent invention are not limited in this respect. As illustrated inFIG. 1, one way that clinician 120 may provide a spoken dictation of thefree-form narration may be to speak the dictation into a microphone 112providing input (e.g., via a direct wired connection, a direct wirelessconnection, or via a connection through an intermediate device) to userinterface 110. An audio recording of the spoken dictation may then bestored in any suitable data format, and transmitted to system 100 and/orto medical transcriptionist 130. Another way that clinician 120 mayprovide the spoken dictation may be to speak into a telephone 118, fromwhich an audio signal may be transmitted to be recorded at system 100,at the site of medical transcriptionist 130, or at any other suitablelocation. Alternatively, the audio signal may be recorded in anysuitable data format at an intermediate facility, and the audio data maythen be relayed to system 100 and/or to medical transcriptionist 130.

In some embodiments, medical transcriptionist 130 may receive the audiorecording of the dictation provided by clinician 120, and may transcribeit into a textual representation of the free-form narration (e.g., intoa text narrative). Medical transcriptionist 130 may be any human wholistens to the audio dictation and writes or types what was spoken intoa text document. In some embodiments, medical transcriptionist 130 maybe specifically trained in the field of medical transcription, and maybe well-versed in medical terminology. In some embodiments, medicaltranscriptionist 130 may transcribe exactly what she hears in the audiodictation, while in other embodiments, medical transcriptionist 130 mayadd formatting to the text transcription to comply with generallyaccepted medical document standards. When medical transcriptionist 130has completed the transcription of the free-form narration into atextual representation, the resulting text narrative may in someembodiments be transmitted to system 100 or any other suitable location(e.g., to a storage location accessible to system 100). Specifically, insome embodiments the text narrative may be received from medicaltranscriptionist 130 by fact extraction component 104 within system 100.Exemplary functionality of fact extraction component 104 is describedbelow.

In some other embodiments, the audio recording of the spoken dictationmay be received, at system 100 or any other suitable location, byautomatic speech recognition (ASR) engine 102. In some embodiments, ASRengine 102 may then process the audio recording to determine what wasspoken. As discussed above, such processing may involve any suitablespeech recognition technique, as aspects of the present invention arenot limited in this respect. In some embodiments, the audio recordingmay be automatically converted to a textual representation, while inother embodiments, words identified directly from the audio recordingmay be represented in a data format other than text, or abstractconcepts may be identified instead of words. Examples of furtherprocessing are described below with reference to a text narrative thatis a textual representation of the free-form narration; however, itshould be appreciated that similar processing may be performed on otherrepresentations of the free-form narration as discussed above. When atextual representation is produced, in some embodiments it may bereviewed by a human (e.g., a transcriptionist) for accuracy, while inother embodiments the output of ASR engine 102 may be accepted asaccurate without human review. As discussed above, some embodiments arenot limited to any particular method for transcribing audio data; anaudio recording of a spoken dictation may be transcribed manually by ahuman transcriptionist, automatically by ASR, or semiautomatically byhuman editing of a draft transcription produced by ASR. Transcriptionsproduced by ASR engine 102 and/or by transcriptionist 130 may be encodedor otherwise represented as data in any suitable form, as aspects of theinvention are not limited in this respect.

In some embodiments, ASR engine 102 may make use of a lexicon of medicalterms (which may be part of, or in addition to, another more generalspeech recognition lexicon) while determining the sequence of words thatwere spoken in the free-form narration provided by clinician 120.However, aspects of the invention are not limited to the use of alexicon, or any particular type of lexicon, for ASR. When used, themedical lexicon in some embodiments may be linked to a knowledgerepresentation model such as a clinical language understanding ontologyutilized by fact extraction component 104, such that ASR engine 102might produce a text narrative containing terms in a form understandableto fact extraction component 104. In some embodiments, a more generalspeech recognition lexicon might also be shared between ASR engine 102and fact extraction component 104. However, in other embodiments, ASRengine 102 may not have any lexicon developed to be in common with factextraction component 104. In some embodiments, a lexicon used by ASRengine 102 may be linked to a different type of medical knowledgerepresentation model, such as one not designed or used for languageunderstanding. It should be appreciated that any lexicon used by ASRengine 102 and/or fact extraction component 104 may be implementedand/or represented as data in any suitable way, as aspects of theinvention are not limited in this respect.

In some embodiments, a text narrative, whether produced by ASR engine102 (and optionally verified or not by a human), produced by medicaltranscriptionist 130, directly entered in textual form through userinterface 110, or produced in any other way, may be re-formatted in oneor more ways before being received by fact extraction component 104.Such re-formatting may be performed by ASR engine 102, by a component offact extraction component 104, by a combination of ASR engine 102 andfact extraction component 104, or by any other suitable software and/orhardware component. In some embodiments, the re-formatting may beperformed in a way known to facilitate fact extraction, and may beperformed for the purpose of facilitating the extraction of clinicalfacts from the text narrative by fact extraction component 104. Forexample, in some embodiments, processing to perform fact extraction maybe improved if sentence boundaries in the text narrative are accurate.Accordingly, in some embodiments, the text narrative may be re-formattedprior to fact extraction to add, remove or correct one or more sentenceboundaries within the text narrative. In some embodiments, this mayinvolve altering the punctuation in at least one location within thetext narrative. In another example, fact extraction may be improved ifthe text narrative is organized into sections with headings, and thusthe re-formatting may include determining one or more section boundariesin the text narrative and adding, removing or correcting one or morecorresponding section headings. In some embodiments, the re-formattingmay include normalizing one or more section headings (which may havebeen present in the original text narrative and/or added or corrected aspart of the re-formatting) according to a standard for the healthcareinstitution corresponding to the patient encounter (which may be aninstitution-specific standard or a more general standard for sectionheadings in clinical documents). In some embodiments, a user (such asclinician 120, medical transcriptionist 130, or another user) may beprompted to approve the re-formatted text.

In some embodiments, either an original or a re-formatted text narrativemay be received by fact extraction component 104, which may performprocessing to extract one or more medical facts (e.g., clinical facts)from the text narrative. The text narrative may be received from ASRengine 102, from medical transcriptionist 130, directly from clinician120 via user interface 110, or in any other suitable way. Any suitabletechnique(s) for extracting facts from the text narrative may be used,as aspects of the present invention are not limited in this respect.Exemplary techniques for medical fact extraction are described below.

In some embodiments, a fact extraction component may be implementedusing techniques such as those described in U.S. Pat. No. 7,493,253,entitled “Conceptual World Representation Natural Language UnderstandingSystem and Method.” U.S. Pat. No. 7,493,253 is incorporated herein byreference in its entirety. Such a fact extraction component may make useof a formal ontology linked to a lexicon of clinical terms. The formalontology may be implemented as a relational database, or in any othersuitable form, and may represent semantic concepts relevant to themedical domain, as well as linguistic concepts related to ways thesemantic concepts may be expressed in natural language.

In some embodiments, concepts in a formal ontology used by a factextraction component may be linked to a lexicon of medical terms and/orcodes, such that each medical term and each code is linked to at leastone concept in the formal ontology. In some embodiments, the lexicon mayinclude the standard medical terms and/or codes used by the institutionin which the fact extraction component is applied. For example, thestandard medical terms and/or codes used by an EHR maintained by theinstitution may be included in the lexicon linked to the fact extractioncomponent's formal ontology. In some embodiments, the lexicon may alsoinclude additional medical terms used by the various clinicians withinthe institution, and/or used by clinicians generally, when describingmedical issues in a free-form narration. Such additional medical termsmay be linked, along with their corresponding standard medical terms, tothe appropriate shared concepts within the formal ontology. For example,the standard term “acute myocardial infarction” as well as othercorresponding terms such as “heart attack”, “acute MI” and “AMI” may allbe linked to the same abstract concept in the formal ontology—a conceptrepresenting an interruption of blood supply to the heart. Such linkageof multiple medical terms to the same abstract concept in someembodiments may relieve the clinician of the burden of ensuring thatonly standard medical terms preferred by the institution appear in thefree-form narration. For example, in some embodiments, a clinician maybe free to use the abbreviation “AMI” or the colloquial “heart attack”in his free-form narration, and the shared concept linkage may allow thefact extraction component to nevertheless automatically extract a factcorresponding to “acute myocardial infarction”.

In some embodiments, a formal ontology used by a fact extractioncomponent may also represent various types of relationships between theconcepts represented. One type of relationship between two concepts maybe a parent-child relationship, in which the child concept is a morespecific version of the parent concept. More formally, in a parent-childrelationship, the child concept inherits all necessary properties of theparent concept, while the child concept may have necessary propertiesthat are not shared by the parent concept. For example, “heart failure”may be a parent concept, and “congestive heart failure” may be a childconcept of “heart failure.” In some embodiments, any other type(s) ofrelationship useful to the process of medical documentation may also berepresented in the formal ontology. For example, one type ofrelationship may be a symptom relationship. In one example of a symptomrelationship, a concept linked to the term “chest pain” may have arelationship of “is-symptom-of” to the concept linked to the term “heartattack”. Other types of relationships may include complicationrelationships, comorbidity relationships, interaction relationships(e.g., among medications), and many others. Any number and type(s) ofconcept relationships may be included in such a formal ontology, asaspects of the present invention are not limited in this respect.

In some embodiments, automatic extraction of medical facts from aclinician's free-form narration may involve parsing the free-formnarration to identify medical terms that are represented in the lexiconof the fact extraction component. Concepts in the formal ontology linkedto the medical terms that appear in the free-form narration may then beidentified, and concept relationships in the formal ontology may betraced to identify further relevant concepts. Through theserelationships, as well as the linguistic knowledge represented in theformal ontology, one or more medical facts may be extracted. Forexample, if the free-form narration includes the medical term“hypertension” and the linguistic context relates to the patient's past,the fact extraction component may automatically extract a factindicating that the patient has a history of hypertension. On the otherhand, if the free-form narration includes the medical term“hypertension” in a sentence about the patient's mother, the factextraction component may automatically extract a fact indicating thatthe patient has a family history of hypertension. In some embodiments,relationships between concepts in the formal ontology may also allow thefact extraction component to automatically extract facts containingmedical terms that were not explicitly included in the free-formnarration. For example, the medical term “meningitis” can also bedescribed as inflammation in the brain. If the free-form narrationincludes the terms “inflammation” and “brain” in proximity to eachother, then relationships in the formal ontology between concepts linkedto the terms “inflammation”, “brain” and “meningitis” may allow the factextraction component to automatically extract a fact corresponding to“meningitis”, despite the fact that the term “meningitis” was not statedin the free-form narration.

It should be appreciated that the foregoing descriptions are provided byway of example only, and that any suitable technique(s) for extracting aset of one or more medical facts from a free-form narration may be used,as aspects of the present invention are not limited to any particularfact extraction technique. For instance, it should be appreciated thatfact extraction component 104 is not limited to the use of an ontology,as other forms of knowledge representation models, including statisticalmodels and/or rule-based models, may also be used. The knowledgerepresentation model may also be represented as data in any suitableformat, and may be stored in any suitable location, such as in a storagemedium of system 100 accessible by fact extraction component 104, asaspects of the invention are not limited in this respect. In addition, aknowledge representation model such as an ontology used by factextraction component 104 may be constructed in any suitable way, asaspects of the invention are not limited in this respect.

For instance, in some embodiments a knowledge representation model maybe constructed manually by one or more human developers with access toexpert knowledge about medical facts, diagnoses, problems, potentialcomplications, comorbidities, appropriate observations and/or clinicalfindings, and/or any other relevant information. In other embodiments, aknowledge representation model may be generated automatically, forexample through statistical analysis of past medical reports documentingpatient encounters, of medical literature and/or of other medicaldocuments. Thus, in some embodiments, fact extraction component 104 mayhave access to a data set 170 of medical literature and/or otherdocuments such as past patient encounter reports. In some embodiments,past reports and/or other text documents may be marked up (e.g., by ahuman) with labels indicating the nature of the relevance of particularstatements in the text to the patient encounter or medical topic towhich the text relates. A statistical knowledge representation model maythen be trained to form associations based on the prevalence ofparticular labels corresponding to similar text within an aggregate setof multiple marked up documents. For example, if “pneumothorax” islabeled as a “complication” in a large enough proportion of clinicalprocedure reports documenting pacemaker implantation procedures, astatistical knowledge representation model may generate and store aconcept relationship that “pneumothorax is-complication-of pacemakerimplantation.” In some embodiments, automatically generated and hardcoded (e.g., by a human developer) concepts and/or relationships mayboth be included in a knowledge representation model used by factextraction component 104.

As discussed above, it should be appreciated that aspects of theinvention are not limited to any particular technique(s) forconstructing knowledge representation models. Examples of suitabletechniques include those disclosed in the following:

-   Gómez-Pérez, A., and Manzano-Macho, D. (2005). An overview of    methods and tools for ontology learning from texts. Knowledge    Engineering Review 19, p. 187-212.-   Cimiano, P., and Staab, S. (2005). Learning concept hierarchies from    text with a guided hierarchical clustering algorithm. In C. Biemann    and G. Paas (eds.), Proceedings of the ICML 2005 Workshop on    Learning and Extending Lexical Ontologies with Machine Learning    Methods, Bonn, Germany.-   Fan, J., Ferrucci, D., Gondek, D., and Kalyanpur, A. (2010).    PRISMATIC: Inducing Knowledge from a Lange Scale Lexicalized    Relation Resource. NAACL Workshop on Formalisms and Methodology for    Learning by Reading.-   Welty, C., Fan, J., Gondek, D. and Schlaikjer, A. (2010). Large    scale relation detection. NAACL Workshop on Formalisms and    Methodology for Learning by Reading.

Each of the foregoing publications is incorporated herein by referencein its entirety.

Alternatively or additionally, in some embodiments a fact extractioncomponent may make use of one or more statistical models to extractsemantic entities from natural language input. In general, a statisticalmodel can be described as a functional component designed and/or trainedto analyze new inputs based on probabilistic patterns observed in priortraining inputs. In this sense, statistical models differ from“rule-based” models, which typically apply hard-coded deterministicrules to map from inputs having particular characteristics to particularoutputs. By contrast, a statistical model may operate to determine aparticular output for an input with particular characteristics byconsidering how often (e.g., with what probability) training inputs withthose same characteristics (or similar characteristics) were associatedwith that particular output in the statistical model's training data. Tosupply the probabilistic data that allows a statistical model toextrapolate from the tendency of particular input characteristics to beassociated with particular outputs in past examples, statistical modelsare typically trained (or “built”) on large training corpuses with greatnumbers of example inputs. Typically the example inputs are labeled withthe known outputs with which they should be associated, usually by ahuman labeler with expert knowledge of the domain. Characteristics ofinterest (known as “features”) are identified (“extracted”) from theinputs, and the statistical model learns the probabilities with whichdifferent features are associated with different outputs, based on howoften training inputs with those features are associated with thoseoutputs. When the same features are extracted from a new input (e.g., aninput that has not been labeled with a known output by a human), thestatistical model can then use the learned probabilities for theextracted features (as learned from the training data) to determinewhich output is most likely correct for the new input. Exemplaryimplementations of a fact extraction component using one or morestatistical models are described further below.

In some embodiments, fact extraction component 104 may utilize astatistical fact extraction model based on entity detection and/ortracking techniques, such as those disclosed in: Florian, R., Hassan,H., Ittycheriah, A., Jing, H., Kambhatla, N., Luo, X., Nicolov, N., andRoukos, S. (2004). A Statistical Model for Multilingual Entity Detectionand Tracking. Proceedings of the Human Language Technologies Conference2004 (HLT-NAACL '04). This publication is incorporated herein byreference in its entirety.

For example, in some embodiments, a list of fact types of interest forgenerating medical reports may be defined, e.g., by a developer of factextraction component 104. Such fact types (also referred to herein as“entity types”) may include, for example, problems, disorders (adisorder is a type of problem), diagnoses (a diagnosis may be a disorderthat a clinician has identified as a problem for a particular patient),findings (a finding is a type of problem that need not be a disorder),medications, body sites, social history facts, allergies, diagnostictest results, vital signs, procedures, procedure steps, observations,devices, and/or any other suitable medical fact types. It should beappreciated that any suitable list of fact types may be utilized, andmay or may not include any of the fact types listed above, as aspects ofthe invention are not limited in this respect. In some embodiments,spans of text in a set of sample patient encounter reports may belabeled (e.g., by a human) with appropriate fact types from the list. Astatistical model may then be trained on the corpus of labeled samplereports to detect and/or track such fact types as semantic entities,using entity detection and/or tracking techniques, examples of which aredescribed below.

For example, in some embodiments, a large number of past free-formnarrations created by clinicians may be manually labeled to form acorpus of training data for a statistical entity detection model. Asdiscussed above, in some embodiments, a list of suitable entities may bedefined (e.g., by a domain administrator) to include medical fact typesthat are to be extracted from future clinician narrations. One or morehuman labelers (e.g., who may have specific knowledge about medicalinformation and typical clinician narration content) may then manuallylabel portions of the training texts with the particular definedentities to which they correspond. For example, given the training text,“Patient is complaining of acute sinusitis,” a human labeler may labelthe text portion “acute sinusitis” with the entity label “Problem.” Inanother example, given the training text, “He has sinusitis, whichappears to be chronic,” a human labeler may label the text “sinusitis”and “chronic” with a single label indicating that both words togethercorrespond to a “Problem” entity. As should be clear from theseexamples, the portion of the text labeled as corresponding to a singleconceptual entity need not be formed of contiguous words, but may havewords split up within the text, having non-entity words in between.

In some embodiments, the labeled corpus of training data may then beprocessed to build a statistical model trained to detect mentions of theentities labeled in the training data. Each time the same conceptualentity appears in a text, that appearance is referred to as a mention ofthat entity. For example, consider the text, “Patient has sinusitis. Hissinusitis appears to be chronic.” In this example, the entity detectionmodel may be trained to identify each appearance of the word “sinusitis”in the text as a separate mention of the same “Problem” entity.

In some embodiments, the process of training a statistical entitydetection model on labeled training data may involve a number of stepsto analyze each training text and probabilistically associate itscharacteristics with the corresponding entity labels. In someembodiments, each training text (e.g., free-form clinician narration)may be tokenized to break it down into various levels of syntacticsubstructure. For example, in some embodiments, a tokenizer module maybe implemented to designate spans of the text as representingstructural/syntactic units such as document sections, paragraphs,sentences, clauses, phrases, individual tokens, words, sub-word unitssuch as affixes, etc. In some embodiments, individual tokens may oftenbe single words, but some tokens may include a sequence of more than oneword that is defined, e.g., in a dictionary, as a token. For example,the term “myocardial infarction” could be defined as a token, althoughit is a sequence of more than one word. In some embodiments, a token'sidentity (i.e., the word or sequence of words itself) may be used as afeature of that token. In some embodiments, the token's placement withinparticular syntactic units in the text (e.g., its section, paragraph,sentence, etc.) may also be used as features of the token.

In some embodiments, an individual token within the training text may beanalyzed (e.g., in the context of the surrounding sentence) to determineits part of speech (e.g., noun, verb, adjective, adverb, preposition,etc.), and the token's part of speech may be used as a further featureof that token. In some embodiments, each token may be tagged with itspart of speech, while in other embodiments, not every token may betagged with a part of speech. In some embodiments, a list of relevantparts of speech may be pre-defined, e.g., by a developer of thestatistical model, and any token having a part of speech listed asrelevant may be tagged with that part of speech. In some embodiments, aparser module may be implemented to determine the syntactic structure ofsentences in the text, and to designate positions within the sentencestructure as features of individual tokens. For example, in someembodiments, the fact that a token is part of a noun phrase or a verbphrase may be used as a feature of that token. Any type of parser may beused, non-limiting examples of which include a bottom-up parser and/or adependency parser, as aspects of the invention are not limited in thisrespect.

In some embodiments, section membership may be used as a feature of atoken. In some embodiments, a section normalization module may beimplemented to associate various portions of the narrative text with theproper section to which it should belong. In some embodiments, a set ofstandardized section types (e.g., identified by their section headings)may be defined for all texts, or a different set of normalized sectionheadings may be defined for each of a number of different types of texts(e.g., corresponding to different types of documents). For example, insome embodiments, a different set of normalized section headings may bedefined for each type of medical document in a defined set of medicaldocument types. Non-limiting examples of medical document types includeconsultation reports, history & physical reports, discharge summaries,and emergency room reports, although there are also many other examples.In the medical field, the various types of medical documents are oftenreferred to as “work types.” In some cases, the standard set of sectionsfor various types of medical documents may be established by a suitablesystem standard, institutional standard, or more widely applicablestandard, such as the Meaningful Use standard (discussed above) or theLogical Observation Identifiers Names and Codes (LOINC) standardmaintained by the Regenstrief Institute. For example, an expected set ofsection headings for a history & physical report under the MeaningfulUse standard may include headings for a “Reason for Visit” section, a“History of Present Illness” section, a “History of Medication Use”section, an “Allergies, Adverse Reactions and Alerts” section, a “Reviewof Systems” section, a “Social History” section, a “Physical Findings”section, an “Assessment and Plan” section, and/or any other suitablesection(s). Any suitable set of sections may be used, however, asaspects of the invention are not limited in this respect.

A section normalization module may use any suitable technique toassociate portions of text with normalized document sections, as aspectsof the invention are not limited in this respect. In some embodiments,the section normalization module may use a table (e.g., stored as datain a storage medium) to map text phrases that commonly occur in medicaldocuments to the sections to which they should belong. In anotherexample, a statistical model may be trained to determine the most likelysection for a portion of text based on its semantic content, thesemantic content of surrounding text portions, and/or the expectedsemantic content of the set of normalized sections. In some embodiments,once a normalized section for a portion of text has been identified, themembership in that section may be used as a feature of one or moretokens in that portion of text.

In some embodiments, other types of features may be extracted, i.e.,identified and associated with tokens in the training text. For example,in some embodiments, an N-gram feature may identify the previous (N−1)words and/or tokens in the text as a feature of the current token. Inanother example, affixes (e.g., suffixes such as -ectomy, -oma, -itis,etc.) may be used as features of tokens. In another example, one or morepredefined dictionaries and/or ontologies may be accessed, and a token'smembership in any of those dictionaries may be used as a feature of thattoken. For example, a predefined dictionary of surgical procedures maybe accessed, and/or a dictionary of body sites, and/or a dictionary ofknown diseases, etc. It should be appreciated, however, that all of theforegoing feature types are merely examples, and any suitable numberand/or types of features of interest may be designated, e.g., by adeveloper of the statistical entity detection model, as aspects of theinvention are not limited in this respect.

In some embodiments, the corpus of training text with its hand-labeledfact type entity labels, along with the collection of features extractedfor tokens in the text, may be input to the statistical entity detectionmodel for training. As discussed above, examples of suitable featuresinclude position within document structure, syntactic structure, partsof speech, parser features, N-gram features, affixes (e.g., prefixesand/or suffixes), membership in dictionaries (sometimes referred to as“gazetteers”) and/or ontologies, surrounding token contexts (e.g., acertain number of tokens to the left and/or right of the current token),orthographic features (e.g., capitalization, letters vs. numbers, etc.),entity labels assigned to previous tokens in the text, etc. As onenon-limiting example, consider the training sentence, “Patient iscomplaining of acute sinusitis,” for which the word sequence “acutesinusitis” was hand-labeled as being a “Problem” entity. In oneexemplary implementation, features extracted for the token “sinusitis”may include the token identity feature that the word is “sinusitis,” asyntactic feature specifying that the token occurred at the end of asentence (e.g., followed by a period), a part-of-speech feature of“noun,” a parser feature that the token is part of a noun phrase (“acutesinusitis”), a trigram feature that the two preceding words are “ofacute,” an affix feature of “-itis,” and a dictionary feature that thetoken is a member of a predefined dictionary of types of inflammation.It should be appreciated, however, that the foregoing list of featuresis merely exemplary, as any suitable features may be used. Aspects ofthe invention are not limited to any of the features listed above, andimplementations including some, all, or none of the above features, aswell as implementations including features not listed above, arepossible.

In some embodiments, given the extracted features and manual entitylabels for the entire training corpus as input, the statistical entitydetection model may be trained to be able to probabilistically label newtexts (e.g., texts not included in the training corpus) with automaticentity labels using the same feature extraction technique that wasapplied to the training corpus. In other words, by processing the inputfeatures and manual entity labels of the training corpus, thestatistical model may learn probabilistic relationships between thefeatures and the entity labels. When later presented with an input textwithout manual entity labels, the statistical model may then apply thesame feature extraction techniques to extract features from the inputtext, and may apply the learned probabilistic relationships toautomatically determine the most likely entity labels for word sequencesin the input text. Any suitable statistical modeling technique may beused to learn such probabilistic relationships, as aspects of theinvention are not limited in this respect. Non-limiting examples ofsuitable known statistical modeling techniques include machine learningtechniques such as maximum entropy modeling, support vector machines,and conditional random fields, among others.

In some embodiments, training the statistical entity detection model mayinvolve learning, for each extracted feature, a probability with whichtokens having that feature are associated with each entity type. Forexample, for the suffix feature “-itis,” the trained statistical entitydetection model may store a probability p1 that a token with thatfeature should be labeled as being part of a “Problem” entity, aprobability p2 that a token with that feature should be labeled as beingpart of a “Medication” entity, etc. In some embodiments, suchprobabilities may be learned by determining the frequency with whichtokens having the “-itis” feature were hand-labeled with each differententity label in the training corpus. In some embodiments, theprobabilities may be normalized such that, for each feature, theprobabilities of being associated with each possible entity (fact type)may sum to 1. However, aspects of the invention are not limited to suchnormalization. In some embodiments, each feature may also have aprobability p0 of not being associated with any fact type, such that thenon-entity probability p0 plus the probabilities of being associatedwith each possible fact type sum to 1 for a given feature. In otherembodiments, separate classifiers may be trained for each fact type, andthe classifiers may be run in parallel. For example, the “-itis” featuremay have probability p1 of being part of a “Problem” entity andprobability (1−p1) of not being part of a “Problem” entity, probabilityp2 of being part of a “Medication” entity and probability (1−p2) of notbeing part of a “Medication” entity, and so on. In some embodiments,training separate classifiers may allow some word sequences to have anon-zero probability of being labeled with more than one fact typesimultaneously; for example, “kidney failure” could be labeled asrepresenting both a Body Site and a Problem. In some embodiments,classifiers may be trained to identify sub-portions of an entity label.For example, the feature “-itis” could have a probability p_(B) of itstoken being at the beginning of a “Problem” entity label, a probabilityp_(I) of its token being inside a “Problem” entity label (but not at thebeginning of the label), and a probability p_(O) of its token beingoutside a “Problem” entity label (i.e., of its token not being part of a“Problem” entity).

In some embodiments, the statistical entity detection model may befurther trained to weight the individual features of a token todetermine an overall probability that it should be associated with aparticular entity label. For example, if the token “sinusitis” has nextracted features f1 . . . fn having respective probabilities p1 . . .pn of being associated with a “Problem” entity label, the statisticalmodel may be trained to apply respective weights w1 . . . wn to thefeature probabilities, and then combine the weighted featureprobabilities in any suitable way to determine the overall probabilitythat “sinusitis” should be part of a “Problem” entity. Any suitabletechnique for determining such weights may be used, including knownmodeling techniques such as maximum entropy modeling, support vectormachines, conditional random fields, and/or others, as aspects of theinvention are not limited in this respect.

In some embodiments, when an unlabeled text is input to the trainedstatistical entity detection model, the model may process the text toextract features and determine probabilities for individual tokens ofbeing associated with various entity (e.g., fact type) labels. In someembodiments, the most probable label (including the non-entity label, ifit is most probable) may be selected for each token in the input text.In other embodiments, labels may be selected through more contextualanalysis, such as at the phrase level or sentence level, rather than atthe token level. Any suitable technique, such as Viterbi techniques, orany other suitable technique, may be used, as aspects of the inventionare not limited in this respect. In some embodiments, a lattice may beconstructed of the associated probabilities for all entity types for alltokens in a sentence, and the best (e.g., highest combined probability)path through the lattice may be selected to determine which wordsequences in the sentence are to be automatically labeled with whichentity (e.g., fact type) labels. In some embodiments, not only the bestpath may be identified, but also the (N−1)-best alternative paths withthe next highest associated probabilities. In some embodiments, this mayresult in an N-best list of alternative hypotheses for fact type labelsto be associated with the same input text.

In some embodiments, a statistical model may also be trained toassociate fact types extracted from new reports with particular facts tobe extracted from those reports (e.g., to determine a particular conceptrepresented by the text portion that has been labeled as an entitymention). For example, in some embodiments, a statistical factextraction model may be applied to automatically label “acute sinusitis”not only with the “Problem” entity (fact type) label, but also with alabel indicating the particular medical fact (e.g., concept) indicatedby the word sequence (e.g., the medical fact “sinusitis, acute”). Insuch embodiments, for example, a single statistical model may be trainedto detect specific particular facts as individual entities. For example,in some embodiments, the corpus of training text may be manually labeledby one or more human annotators with labels indicating specific medicalfacts, rather than labels indicating more general entities such as facttypes or categories. However, in other embodiments, the process ofdetecting fact types as entities may be separated from the process ofrelating detected fact types to particular facts. For example, in someembodiments, a separate statistical model (e.g., an entity detectionmodel) may be trained to automatically label portions of text with facttype labels, and another separate statistical model (e.g., a relationmodel) may be trained to identify which labeled entity (fact type)mentions together indicate a single specific medical fact. In somecases, the relation model may identify particular medical facts byrelating together two or more mentions labeled with the same entitytype.

For example, in the text, “Patient is complaining of acute sinusitis,”in some embodiments an entity detection model may label the tokens“acute” and “sinusitis” as being part of a “Problem” entity. In someembodiments, a relation model, given that “acute” and “sinusitis” havebeen labeled as “Problem,” may then relate the two tokens together to asingle medical fact of “sinusitis, acute.” For another example, considerthe text, “Patient has sinusitis, which appears to be chronic.” In someembodiments, an entity detection model may be applied to label thetokens “sinusitis” and “chronic” as “Problem” entity mentions. In someembodiments, a relation model may then be applied to determine that thetwo “Problem” entity mentions “sinusitis” and “chronic” are related(even though they are not contiguous in the text) to represent a singlemedical fact of “sinusitis, chronic.” For yet another example, considerthe text, “She has acute sinusitis; chronic attacks of asthma may be afactor.” In some embodiments, an entity detection model may label eachof the tokens “acute,” “sinusitis,” “chronic,” and “asthma” as belongingto “Problem” entity mentions. In some embodiments, a relation model maythen be applied to determine which mentions relate to the same medicalfact. For example, the relation model may determine that the tokens“acute” and “sinusitis” relate to a first medical fact (e.g.,“sinusitis, acute”), while the tokens “chronic” and “asthma” relate to adifferent medical fact (e.g., “asthma, chronic”), even though the token“chronic” is closer in the sentence to the token “sinusitis” than to thetoken “asthma.”

In some embodiments, a relation model may be trained statistically usingmethods similar to those described above for training the statisticalentity detection model. For example, in some embodiments, training textsmay be manually labeled with various types of relations between entitymentions and/or tokens within entity mentions. For example, in thetraining text, “Patient has sinusitis, which appears to be chronic,” ahuman annotator may label the “Problem” mention “chronic” as having arelation to the “Problem” mention “sinusitis,” since both mentions referto the same medical fact. In some embodiments, the relation annotationsmay simply indicate that certain mentions are related to each other,without specifying any particular type of relationship. In otherembodiments, relation annotations may also indicate specific types ofrelations between entity mentions. Any suitable number and/or types ofrelation annotations may be used, as aspects of the invention are notlimited in this respect. For example, in some embodiments, one type ofrelation annotation may be a “split” relation label. The tokens“sinusitis” and “chronic,” for example, may be labeled as having a splitrelationship, because “sinusitis” and “chronic” together make up anentity, even though they are not contiguous within the text. In thiscase, “sinusitis” and “chronic” together indicate a specific type ofsinusitis fact, i.e., one that it is chronic and not, e.g., acute.Another exemplary type of relation may be an “attribute” relation. Insome embodiments, one or more system developers may define sets ofattributes for particular fact types, corresponding to relatedinformation that may be specified for a fact type. For example, a“Medication” fact type may have attributes “dosage,” “route,”“frequency,” “duration,” etc. In another example, an “Allergy” fact typemay have attributes “allergen,” “reaction,” “severity,” etc. It shouldbe appreciated, however, that the foregoing are merely examples, andthat aspects of the invention are not limited to any particularattributes for any particular fact types. Also, other types of factrelations are possible, including family relative relations,causes-problem relations, improves-problem relations, and many others.Aspects of the invention are not limited to use of any particularrelation types.

In some embodiments, using techniques similar to those described above,the labeled training text may be used as input to train the statisticalrelation model by extracting features from the text, andprobabilistically associating the extracted features with the manuallysupplied labels. Any suitable set of features may be used, as aspects ofthe invention are not limited in this respect. For example, in someembodiments, features used by a statistical relation model may includeentity (e.g., fact type) labels, parts of speech, parser features,N-gram features, token window size (e.g., a count of the number of wordsor tokens present between two tokens that are being related to eachother), and/or any other suitable features. It should be appreciated,however, that the foregoing features are merely exemplary, asembodiments are not limited to any particular list of features. In someembodiments, rather than outputting only the best (e.g., most probable)hypothesis for relations between entity mentions, a statistical relationmodel may output a list of multiple alternative hypotheses, e.g., withcorresponding probabilities, of how the entity mentions labeled in theinput text are related to each other. In yet other embodiments, arelation model may be hard-coded and/or otherwise rule-based, while theentity detection model used to label text portions with fact types maybe trained statistically.

In some embodiments, the relation model or another statistical model mayalso be trained to track mentions of the same entity from differentsentences and/or document sections and to relate them together.Exemplary techniques for entity tracking are described in thepublication by Florian cited above.

In some embodiments, further processing may be applied to normalizeparticular facts extracted from the text to standard forms and/or codesin which they are to be documented. For example, medical personnel oftenhave many different ways of phrasing the same medical fact, and anormalization/coding process in some embodiments may be applied toidentify the standard form and/or code corresponding to each extractedmedical fact that was stated in a non-standard way. The standard formand/or code may be derived from any suitable source, as aspects of theinvention are not limited in this respect. Some standard terms and/orcodes may be derived from a government or profession-wide standard, suchas SNOMED (Systematized Nomenclature of Medicine), UMLS (Unified MedicalLanguage System), RxNorm, RadLex, etc. Other standard terms and/or codesmay be more locally derived, such as from standard practices of aparticular locality or institution. Still other standard terms and/orcodes may be specific to the documentation system including the factextraction component being applied.

For example, given the input text, “His sinuses are constantlyinflamed,” in some embodiments, an entity detection model together witha relation model (or a single model performing both functions) mayidentify the tokens “sinuses,” “constantly” and “inflamed” asrepresenting a medical fact. In some embodiments, a normalization/codingprocess may then be applied to identify the standard form fordocumenting “constantly inflamed sinuses” as “sinusitis, chronic.”Alternatively or additionally, in some embodiments thenormalization/coding process may identify a standard code used todocument the identified fact. For example, the ICD-9 code for“sinusitis, chronic” is ICD-9 code #473. Any suitable coding system maybe used, as aspects of the invention are not limited in this respect.Exemplary standard codes include ICD (International Classification ofDiseases) codes, CPT (Current Procedural Terminology) codes, E&M(Evaluation and Management) codes, MedDRA (Medical Dictionary forRegulatory Activities) codes, SNOMED codes, LOINC (Logical ObservationIdentifiers Names and Codes) codes, RxNorm codes, NDC (National DrugCode) codes and RadLex codes.

In some embodiments, a normalization/coding process may be rule-based(e.g., using lists of possible ways of phrasing particular medicalfacts, and/or using an ontology of medical terms and/or other languageunits to normalize facts extracted from input text to their standardforms). For example, in some embodiments, the tokens identified in thetext as corresponding to a medical fact may be matched to correspondingterms in an ontology. In some embodiments, a list of closest matchingterms may be generated, and may be ranked by their similarity to thetokens in the text. The similarity may be scored in any suitable way.For example, in one suitable technique, one or more tokens in the textmay be considered as a vector of its component elements, such as words,and each of the terms in the ontology may also be considered as a vectorof component elements such as words. Similarity scores between thetokens may then be computed by comparing the corresponding vectors,e.g., by calculating the angle between the vectors, or a relatedmeasurement such as the cosine of the angle. In some embodiments, one ormore concepts that are linked in the ontology to one or more of thehigher ranking terms (e.g., the terms most similar to the identifiedtokens in the text) may then be identified as hypotheses for the medicalfact to be extracted from that portion of the text. Exemplary techniquesthat may be used in some embodiments are described in Salton, Wong, &Yang: “A vector space model for automatic indexing,” Communications ofthe ACM, November 1975. This publication is incorporated herein byreference in its entirety. However, these are merely examples, and anysuitable technique(s) for normalizing entity tokens to standard termsmay be utilized in some embodiments, as aspects of the invention are notlimited in this respect.

In some embodiments, the normalization/coding process may output asingle hypothesis for the standard form and/or code corresponding toeach extracted fact. For example, the single output hypothesis maycorrespond to the concept linked in the ontology to the term that ismost similar to the token(s) in the text from which the fact isextracted. However, in other embodiments, the normalization/codingprocess may output multiple alternative hypotheses, e.g., withcorresponding probabilities, for the standard form and/or codecorresponding to an individual extracted fact. Thus, it should beappreciated that in some embodiments multiple alternative hypotheses fora medical fact to be extracted from a portion of input text may beidentified by fact extraction component 104. Such alternative hypothesesmay be collected at any or all of various processing levels of factextraction, including entity detection, entity relation, and/ornormalization/coding stages. In some embodiments, the list ofalternative hypotheses may be thresholded at any of the various levels,such that the final list output by fact extraction component 104 mayrepresent the N-best alternative hypotheses for a particular medicalfact to be extracted.

It should be appreciated that the foregoing are merely examples, andthat fact extraction component 104 may be implemented in any suitableway and/or form, as aspects of the invention are not limited in thisrespect.

In some embodiments, a user such as clinician 120 may monitor, controland/or otherwise interact with the fact extraction and/or fact reviewprocess through a user interface provided in connection with system 100.For example, in some embodiments, user interface 140 may be provided byfact review component 106, e.g., through execution (e.g., by one or moreprocessors of system 100) of programming instructions incorporated infact review component 106. One exemplary implementation of such a userinterface is graphical user interface (GUI) 200, illustrated in FIG. 2.In some embodiments, when the user is clinician 120, GUI 200 may bepresented via user interface 110. In some embodiments, a user may be aperson other than a clinician; for example, another person such ascoding specialist 150 may be presented with GUI 200 via user interface140. However, it should be appreciated that “user,” as used herein,refers to an end user of system 100, as opposed to a software and/orhardware developer of any component of system 100.

The user interface is not limited to a graphical user interface, asother ways of providing data from system 100 to users may be used. Forexample, in some embodiments, audio indicators may be transmitted fromsystem 100 and conveyed to a user. It should be appreciated that anytype of user interface may be provided in connection with factextraction, fact review and/or other related processes, as aspects ofthe invention are not limited in this respect. While the exemplaryembodiments illustrated in FIG. 1 involve data processing at system 100and data communication between system 100 and user interfaces 110 and/or140, it should be appreciated that in other embodiments any or allprocessing components of system 100 may instead be implemented locallyat user interface 110 and/or user interface 140, as aspects of theinvention are not limited to any particular distribution of local and/orremote processing capabilities.

As depicted in FIG. 2, GUI 200 includes a number of separate panesdisplaying different types of data. Identifying information pane 210includes general information identifying patient 222 as a male patientnamed John Doe. Such general patient identifying information may beentered by clinician 120, or by other user 150, or may be automaticallypopulated from an electronic medical record for patient 122, or may beobtained from any other suitable source. Identifying information pane210 also displays the creation date and document type of the reportcurrently being worked on. This information may also be obtained fromany suitable source, such as from stored data or by manual entry. Whenreferring herein to entry of data by clinician 120 and/or other user150, it should be appreciated that any suitable form of data entry maybe used, including input via mouse, keyboard, touchscreen, stylus,voice, or any other suitable input form, as aspects of the invention arenot limited in this respect.

GUI 200 as depicted in FIG. 2 includes a text panel 220 in which a textnarrative referring to the encounter between clinician 120 and patient122 is displayed. In some embodiments, text panel 220 may include texteditor functionality, such that clinician 120 may directly enter thetext narrative into text panel 220, either during the patient encounteror at some time thereafter. If ASR is used to produce the text narrativefrom a spoken dictation provided by clinician 120, in some embodimentsthe text may be displayed in text panel 220 as it is produced by ASRengine 102, either in real time while clinician 120 is dictating, orwith a larger processing delay. In other embodiments, the text narrativemay be received as stored data from another source, such as from medicaltranscriptionist 130, and may be displayed in completed form in textpanel 220. In some embodiments, the text narrative may then be edited ifdesired by clinician 120 and/or other user 150 within text panel 220.However, text editing capability is not required, and in someembodiments text panel 220 may simply display the text narrative withoutproviding the ability to edit it.

Exemplary GUI 200 further includes a fact panel 230 in which one or moremedical facts, once extracted from the text narrative and/or entered inanother suitable way, may be displayed as discrete structured dataitems. When clinician 120 and/or other user 150 is ready to direct factextraction component 104 to extract one or more medical facts from thetext narrative, in some embodiments he or she may select process button240 via any suitable selection input method. However, a user indicationto begin fact extraction is not limited to a button such as processbutton 240, as any suitable way to make such an indication may beprovided by GUI 200. In some embodiments, no user indication to beginfact extraction may be required, and fact extraction component 104 maybegin a fact extraction process as soon as a requisite amount of text(e.g., enough text for fact extraction component 104 to identify one ormore clinical facts that can be ascertained therefrom) is entered and/orreceived. In some embodiments, a user may select process button 240 tocause fact extraction to be performed before the text narrative iscomplete. For example, clinician 120 may dictate, enter via manual inputand/or otherwise provide a part of the text narrative, select processbutton 240 to have one or more facts extracted from that part of thetext narrative, and then continue to provide further part(s) of the textnarrative. In another example, clinician 120 may provide all or part ofthe text narrative, select process button 240 and review the resultingextracted facts, edit the text narrative within text pane 220, and thenselect process button 240 again to review how the extracted facts maychange.

In some embodiments, one or more medical facts extracted from the textnarrative by fact extraction component 104 may be displayed to the uservia GUI 200 in fact panel 230. Screenshots illustrating an exampledisplay of medical facts extracted from an example text narrative areprovided in FIGS. 3A and 3B. FIG. 3A is a screenshot with fact panel 230scrolled to the top of a display listing medical facts extracted fromthe example text narrative, and FIG. 3B is a screenshot with fact panel230 scrolled to the bottom of the display listing the extracted medicalfacts. In some embodiments, as depicted in FIGS. 3A and 3B, medicalfacts corresponding to a patient encounter may be displayed in factpanel 230, and organized into a number of separate categories of typesof facts. An exemplary set of medical fact categories includescategories for problems, medications, allergies, social history,procedures and vital signs. However, it should be appreciated that anysuitable fact categories may be used, as aspects of the invention arenot limited in this respect. In addition, organization of facts intocategories is not required, and displays without such organization arepossible. As depicted in FIGS. 3A and 3B, in some embodiments GUI 200may be configured to provide a navigation panel 300, with a selectableindication of each fact category available in the display of fact panel230. In some embodiments, when the user selects one of the categorieswithin navigation panel 300 (e.g., by clicking on it with a mouse,touchpad, stylus, or other input device), fact panel 230 may be scrolledto display the corresponding fact category. As depicted in FIGS. 3A and3B, all available fact categories for the current document type aredisplayed, even if a particular fact category includes no extracted orotherwise entered medical facts. However, this is not required; in someembodiments, only those fact categories having facts ascertained fromthe patient encounter may be displayed in fact panel 230.

Fact panel 230 scrolled to the top of the display as depicted in FIG. 3Ashows problem fact category 310, medications fact category 320, andallergies fact category 330. Within problem fact category 310, fourclinical facts have been extracted from the example text narrative; noclinical facts have been extracted in medications fact category 320 orin allergies fact category 330. Within problem fact category 310, fact312 indicates that patient 122 is currently presenting with unspecifiedchest pain; that the chest pain is a currently presenting condition isindicated by the status “active”. Fact 314 indicates that patient 122 iscurrently presenting with shortness of breath. Fact 316 indicates thatthe patient has a history (status “history”) of unspecified essentialhypertension. Fact 318 indicates that the patient has a history ofunspecified obesity. As illustrated in FIG. 3A, each clinical fact inproblem fact category 310 has a name field and a status field. In someembodiments, each field of a clinical fact may be a structured componentof that fact represented as a discrete structured data item. In thisexample, the name field may be structured such that only a standard setof medical terms for problems may be available to populate that field.For example, the status field may be structured such that only statusesin the Systematized Nomenclature of Medicine (SNOMED) standard (e.g.,“active” and “history”) may be selected within that field, althoughother standards (or no standard) could be employed. An exemplary list offact categories and their component fields is given below. However, itshould be appreciated that this list is provided by way of example only,as aspects of the invention are not limited to any particularorganizational system for facts, fact categories and/or fact components.

-   -   Exemplary list of fact categories and component fields:    -   Category: Problems. Fields: Name, SNOMED status, ICD code.    -   Category: Medications. Fields: Name, Status, Dose form,        Frequency, Measures, RxNorm code, Administration condition,        Application duration, Dose route.    -   Category: Allergies. Fields: Allergen name, Type, Status, SNOMED        code, Allergic reaction, Allergen RxNorm.    -   Category: Social history—Tobacco use. Fields: Name, Substance,        Form, Status, Qualifier, Frequency, Duration, Quantity, Unit        type, Duration measure, Occurrence, SNOMED code, Norm value,        Value.    -   Category: Social history—Alcohol use. Fields: Name, Substance,        Form, Status, Qualifier, Frequency, Duration, Quantity,        Quantifier, Unit type, Duration measure, Occurrence, SNOMED        code, Norm value, Value.    -   Category: Procedures. Fields: Name, Date, SNOMED code.    -   Category: Vital signs. Fields: Name, Measure, Unit, Unit type,        Date/Time, SNOMED code, Norm value, Value.

In some embodiments, a linkage may be maintained between one or moremedical facts extracted by fact extraction component 104 and theportion(s) of the text narrative from which they were extracted. Asdiscussed above, such a portion of the text narrative may consist of asingle word or may include multiple words, which may be in a contiguoussequence or may be separated from each other by one or more interveningwords, sentence boundaries, section boundaries, or the like. Forexample, fact 312 indicating that patient 122 is currently presentingwith unspecified chest pain may have been extracted by fact extractioncomponent 104 from the words “chest pain” in the text narrative. The“active” status of extracted fact 312 may have been determined by factextraction component 104 based on the appearance of the words “chestpain” in the section of the text narrative with the section heading“Chief complaint”. In some embodiments, fact extraction component 104and/or another processing component may be programmed to maintain (e.g.,by storing appropriate data) a linkage between an extracted fact (e.g.,fact 312) and the corresponding text portion (e.g., “chest pain”).

In some embodiments, GUI 200 may be configured to provide visualindicators of the linkage between one or more facts displayed in factpanel 230 and the corresponding portion(s) of the text narrative in textpanel 220 from which they were extracted. In the example depicted inFIG. 3A, the visual indicators are graphical indicators consisting oflines placed under the appropriate portions of the text narrative intext panel 220. Indicator 313 indicates the linkage between fact 312 andthe words “chest pain” in the “Chief complaint” section of the textnarrative; indicator 315 indicates the linkage between fact 314 and thewords “shortness of breath” in the “Chief complaint” section of the textnarrative; indicator 317 indicates the linkage between fact 316 and theword “hypertensive” in the “Medical history” section of the textnarrative; and indicator 319 indicates the linkage between fact 318 andthe word “obese” in the “Medical history” section of the text narrative.However, these are merely examples of one way in which visual indicatorsmay be provided, as other types of visual indicators may be provided.For example, different or additional types of graphical indicators maybe provided, and/or linked text in text panel 220 may be displayed in adistinctive textual style (e.g., font, size, color, formatting, etc.).Aspects of the invention are not limited to any particular type oflinkage indicator.

In some embodiments, when the textual representation of the free-formnarration provided by clinician 120 has been re-formatted and factextraction has been performed with reference to the re-formattedversion, the original version may nevertheless be displayed in textpanel 220, and linkages may be maintained and/or displayed with respectto the original version. For example, in some embodiments, eachextracted clinical fact may be extracted by fact extraction component104 from a corresponding portion of the re-formatted text, but thatportion of the re-formatted text may have a corresponding portion of theoriginal text of which it is a formatted version. A linkage maytherefore be maintained between that portion of the original text andthe extracted fact, despite the fact actually having been extracted fromthe re-formatted text. In some embodiments, providing an indicator ofthe linkage between the extracted fact and the original text may allowclinician 120 and/or other user 150 to appreciate how the extracted factis related to what was actually said in the free-form narration.However, other embodiments may maintain linkages between extracted factsand the re-formatted text, as an alternative or in addition to thelinkages between the extracted facts and the original text, as aspectsof the invention are not limited in this respect.

Fact panel 230 scrolled to the bottom of the display as depicted in FIG.3B shows social history fact category 340, procedures fact category 350,and vital signs fact category 360. Within social history fact category340, two clinical facts have been extracted; no facts have beenextracted in procedures fact category 350 and vital signs fact category360. Within social history fact category 340, fact 342 indicates thatpatient 122 currently smokes cigarettes with a frequency of one pack perday. Fact 344 indicates that patient 122 currently occasionally drinksalcohol. Indicator 343 indicates that fact 342 was extracted from thewords “He smokes one pack per day” in the “Social history” section ofthe text narrative; and indicator 345 indicates that fact 344 wasextracted from the words “Drinks occasionally” in the “Social history”section of the text narrative. In some embodiments, visual indicatorssuch as indicators 343 and 345 may be of a different textual and/orgraphical style or of a different indicator type than visual indicatorssuch as indicators 313, 315, 317 and 319, to indicate that theycorrespond to a different fact category. For example, in someembodiments indicators 343 and 345 corresponding to social history factcategory 340 may be displayed in a different color than indicators 313,315, 317 and 319 corresponding to problems fact category 310. In someembodiments, linkages for different individual facts may be displayed indifferent textual and/or graphical styles or indicator types to allowthe user to easily appreciate which fact corresponds to which portion ofthe text narrative. For example, in some embodiments indicator 343 maybe displayed in a different color than indicator 345 because theycorrespond to different facts, even though both correspond to the samefact category.

In some embodiments, GUI 200 may be configured to allow the user toselect one or more of the medical facts in fact panel 230, and inresponse to the selection, to provide an indication of the portion(s) ofthe text narrative from which those fact(s) were extracted. An exampleis illustrated in FIG. 4. In this example, fact 312 (“unspecified chestpain”) has been selected by the user in fact panel 230, and in responsevisual indicator 420 of the portion of the text narrative from whichfact 312 was extracted (“chest pain”) is provided. Such a user selectionmay be made in any suitable way, as aspects of the invention are notlimited in this respect. Examples include using an input device (e.g.,mouse, keyboard, touchpad, stylus, etc.) to click on or otherwise selectfact 312, hovering the mouse or other input mechanism above or nearby tofact 312, speaking a selection of fact 312 through voice, and/or anyother suitable selection method. Similarly, in some embodiments GUI 200may be configured to visually indicate the corresponding fact in factpanel 230 when the user selects a portion of the text narrative in textpanel 220. In some embodiments, a visual indicator may include a line orother graphical connector between a fact and its corresponding portionof the text narrative. Any visual indicator may be provided in anysuitable form (examples of which are given above) as aspects of theinvention are not limited in this respect. In addition, aspects of theinvention are not limited to visual indicators, as other forms ofindicators may be provided. For example, in response to a user selectionof fact 312, an audio indicator of the text portion “chest pain” may beprovided in some embodiments. In some embodiments, the audio indicatormay be provided by playing the portion of the audio recording of theclinician's spoken dictation comprising the words “chest pain”. In otherembodiments, the audio indicator may be provided by playing an audioversion of the words “chest pain” generated using automatic speechsynthesis. Any suitable form of indicator or technique for providingindicators may be used, as aspects of the invention are not limited inthis respect.

In some embodiments, GUI 200 may be configured to provide any of variousways for the user to make one or more changes to the set of medicalfacts extracted from the text narrative by fact extraction component 104and displayed in fact panel 230, and these changes may be collected byfact review component 106 and applied to the documentation of thepatient encounter. For example, the user may be allowed to delete a factfrom the set in fact panel 230, e.g., by selecting the “X” optionappearing next to the fact. In some embodiments, the user may be allowedto edit a fact within fact panel 230. In one example, the user may editthe name field of fact 312 by selecting the fact and typing, speaking orotherwise providing a different name for that fact. As depicted in FIG.3A and FIG. 4, in some embodiments the user may edit the status field offact 312 by selecting a different status from the available drop-downmenu, although other techniques for allowing editing of the status fieldare possible. In some embodiments, the user may alternatively oradditionally be allowed to edit a fact by interacting with the textnarrative in text panel 220. For example, the user may add, delete, orchange one or more words in the text narrative, and then the textnarrative may be re-processed by fact extraction component 104 toextract an updated set of medical facts. In some embodiments, the usermay be allowed to select only a part of the text narrative in text panel220 (e.g., by highlighting it), and have fact extraction component 104re-extract facts only from that part, without disturbing facts alreadyextracted from other parts of the text narrative.

In some embodiments, GUI 200 may be configured to provide any of variousways for one or more facts to be added as discrete structured dataitems. As depicted in FIG. 4, GUI 200 in some embodiments may beconfigured to provide an add fact button for each fact categoryappearing in fact panel 230; one such add fact button is add fact button430. When the user selects add fact button 430, in some embodiments GUI200 may provide the user with a way to enter information sufficient topopulate one or more fields of a new fact in that fact category, forexample by displaying pop-up window 500 as depicted in FIG. 5. It shouldbe appreciated that this is merely one example, as aspects of theinvention are not limited to the use of pop-up windows or any otherparticular method for adding a fact. In this example, pop-up window 500includes a title bar 510 that indicates the fact category (“Problems”)to which the new fact will be added. Pop-up window 500 also provides anumber of fields 520 in which the user may enter information to definethe new fact to be added. Fields 520 may be implemented in any suitableform, including as text entry boxes, drop-down menus, radio buttonsand/or checkboxes, as aspects of the invention are not limited to anyparticular way of receiving input defining a fact. Finally, pop-upwindow 500 includes add button 530, which the user may select to add thenewly defined fact to the set of facts corresponding to the patientencounter, thus entering the fact as a discrete structured data item.

In some embodiments, GUI 200 may alternatively or additionally beconfigured to allow the user to add a new fact by selecting a (notnecessarily contiguous) portion of the text narrative in text panel 220,and indicating that a new fact should be added based on that portion ofthe text narrative. This may be done in any suitable way. In oneexample, the user may highlight the desired portion of the textnarrative in text panel 220, and right-click on it with a mouse (orperform another suitable input operation), which may cause thedesignated text to be processed and any relevant facts to be extracted.In other embodiments, the right-click or other input operation may causea menu to appear. In some embodiments the menu may include options toadd the new fact under any of the available fact categories, and theuser may select one of the options to indicate which fact category willcorrespond to the new fact. In some embodiments, an input screen such aspop-up window 500 may then be provided, and the name field may bepopulated with the words selected by the user from the text narrative.The user may then have the option to further define the fact through oneor more of the other available fields, and to add the fact to the set ofmedical facts for the patient encounter as described above.

In some embodiments, the set of medical facts corresponding to thecurrent patient encounter (each of which may have been extracted fromthe text narrative or provided by the user as a discrete structured dataitem) may be added to an existing electronic medical record (such as anEHR) for patient 122, or may be used in generating a new electronicmedical record for patient 122. In some embodiments, clinician 120and/or coding specialist (or other user) 150 may finally approve the setof medical facts before they are included in any patient record;however, aspects of the present invention are not limited in thisrespect. In some embodiments, when there is a linkage between a fact inthe set and a portion of the text narrative, the linkage may bemaintained when the fact is included in the electronic medical record.In some embodiments, this linkage may be made viewable by simultaneouslydisplaying the fact within the electronic medical record and the textnarrative (or at least the portion of the text narrative from which thefact was extracted), and providing an indication of the linkage in anyof the ways described above. Similarly, extracted facts may be includedin other types of patient records, and linkages between the facts in thepatient records and the portions of text narratives from which they wereextracted may be maintained and indicated in any suitable way.

A CLU system in accordance with the techniques described herein may takeany suitable form, as aspects of the present invention are not limitedin this respect. An illustrative implementation of a computer system 600that may be used in connection with some embodiments of the presentinvention is shown in FIG. 6. One or more computer systems such ascomputer system 600 may be used to implement any of the functionalitydescribed above. The computer system 600 may include one or moreprocessors 610 and one or more tangible, non-transitorycomputer-readable storage media (e.g., volatile storage 620 and one ormore non-volatile storage media 630, which may be formed of any suitablenon-volatile data storage media). The processor 610 may control writingdata to and reading data from the volatile storage 620 and thenon-volatile storage device 630 in any suitable manner, as the aspectsof the present invention are not limited in this respect. To perform anyof the functionality described herein, the processor 610 may execute oneor more instructions stored in one or more computer-readable storagemedia (e.g., volatile storage 620), which may serve as tangible,non-transitory computer-readable storage media storing instructions forexecution by the processor 610.

Computer-Assisted Coding (CAC) System

As discussed above, medical coding has conventionally been a manualprocess whereby a human professional (the “coder”) reads all of thedocumentation for a patient encounter and enters the appropriatestandardized codes (e.g., ICD codes, HCPCS codes, etc.) corresponding tothe patient's diagnoses, procedures, etc. The coder is often required tounderstand and interpret the language of the clinical documents in orderto identify the relevant diagnoses, etc., and assign them theircorresponding codes, as the language used in clinical documentationoften varies widely from the standardized descriptions of the applicablecodes. For example, the coder might review a hospital report saying,“The patient coded at 5:23 pm.” The coder must then apply the knowledgethat “The patient coded” is hospital slang for a diagnosis of “cardiacarrest,” which corresponds to ICD-9-CM code 427.5. This diagnosis couldnot have been identified from a simple word search for the term “cardiacarrest,” since that standard term was not actually used in thedocumentation; more complex interpretation is required in this example.

As also discussed above, conventional medical coding systems may providea platform on which the human coder can read the relevant documents fora patient encounter, and an interface via which the human coder canmanually input the appropriate codes to assign to the patient encounter.By contrast, some embodiments described herein may make use of a type ofmedical coding system referred to herein as a “computer-assisted coding”(CAC) system, which may automatically analyze medical documentation fora patient encounter to interpret the document text and derivestandardized codes hypothesized to be applicable to the patientencounter. The automatically derived codes may then be suggested to thehuman coder, clinician, or other user of the CAC system. In someembodiments, the CAC system may make use of an NLU engine to analyze thedocumentation and derive suggested codes, such as through use of one ormore components of a CLU system such as exemplary system 100 describedabove. In some embodiments, the NLU engine may be configured to derivestandardized codes as a type of medical fact extracted from one or moredocuments for the patient encounter, and/or the CLU system may beconfigured to access coding rules corresponding to the standardized codeset(s) and apply the coding rules to extracted medical facts to derivethe corresponding codes.

In some embodiments, the CAC system may be configured to provide a userinterface via which the automatically suggested codes may be reviewed bya user such as a medical coder. The user interface may take on any ofnumerous forms, and aspects of the invention are not limited to anyparticular implementation. Like the user interfaces for the CLU system100 described above, the user interface for the CAC system may providetools that allow a coder to interact with the CAC system in any suitableform, including visual forms, audio forms, combined forms, or any otherform providing the functionality described herein. When the tools areprovided in visual form, their functionality may be accessed in someembodiments through a graphical user interface (GUI), which may beimplemented in any suitable way. An example of a suitable GUI 700 for aCAC system is illustrated in FIG. 7A.

The exemplary GUI 700 provides the user with the ability tosimultaneously view the list of codes for a patient encounter along withthe documentation from which the codes are derived. Some embodiments mayalso allow the user to view structured encounter- or patient-level datasuch as the patient's age, gender, etc. (not shown in FIG. 7A), some orall of which information may be useful in arriving at the appropriatecodes for the patient encounter. In panel 710 is displayed a list ofavailable documents for the patient encounter currently being coded. Inthe example illustrated in FIG. 7A, these include two History & Physicalreports, a Discharge Summary, an Emergency Room Record, a Consultationreport, a Progress Note, and an Operative Report. Indicator 712 showsthat the current document being viewed is the Discharge Summary datedJun. 18, 2014, and this document appears in panel 720 where the user canview the text of the document. Shown in panel 730 is the current list ofcodes for the patient encounter. An indicator 732 shows, for each codein the list, whether the code was automatically suggested or addedmanually by the user. In this particular example, the empty circlesindicate that all of the codes in the current list were automaticallysuggested by the CAC system.

Exemplary GUI 700 also provides the user with the ability to view and/orquery which portion(s) of the available documentation gave rise to thesuggestion of which code(s) in the list of codes for the patientencounter. In some embodiments, any suitable indicator(s) may beprovided of the link between a particular code and the portion(s) of thedocumentation text from which the code was derived. Each automaticallysuggested code may be linked to one or more portions of text from whichthe code was derived, and each linked portion of text may be linked toone or more codes that are derivable from that portion of text. Forinstance, viewing together FIGS. 7A and 7D, which show the DischargeSummary viewed at different scroll locations in panel 720, it can beseen that there are two different mentions of “respiratory failure” inthe document from which code 518.81 may have been derived (an example ofa link between a code and multiple portions of text), and that there aretwo different codes 303.90 and 571.5 that may have been derived at leastin part from the mention of “Alcoholism” in the text (an example of alink between a portion of text and multiple codes).

In the example of FIG. 7A, an indicator 722 is provided (underlining inthis particular example) to visually distinguish portions of thedocument text linked to codes in the current list. Exemplary GUI 700also allows the user to query a particular linked portion of text to seewhich code(s) are linked to that portion of text. FIG. 7B illustrates anexemplary indicator 724 of the corresponding link that may be displayedin response to the user querying the linked portion of text in anysuitable way, such as by selecting or hovering over it with the mousepointer. Exemplary GUI 700 further allows the user to query a particularcode to see which portion(s) of text are linked to that code. FIG. 7Cillustrates an exemplary way of querying code 287.5 by right-clicking onthe listed code in panel 730 and selecting “Show Highlights” in thecontext menu that then appears. In response, the document in which thelinked text appears is displayed in panel 720 (in this case it is thesame Discharge Summary, scrolled to a particular section), and thelinked text is visually distinguished by indicator 726 (highlighting inthis particular example), as illustrated in FIG. 7D.

If the user disagrees with the linked text and does not believe that thesuggested portion(s) of text actually should correspond with the linkedcode, the user can select “Unlink Text” in the context menu of FIG. 7Cto cause the link between that code and the corresponding text to bediscarded. The user can also manually create a new link between a codeand one or more portions of text, e.g., by selecting “Link Text” in thecontext menu of FIG. 7C and highlighting or otherwise designating theportion(s) of text in the documentation which should be linked to theselected code.

Exemplary GUI 700 further allows the user to accept or reject each ofthe automatically suggested codes, e.g., using the context menu of FIG.7C for each suggested code. FIG. 7E illustrates exemplary indicators 734and 736 which replace indicator 732 for each code that has been acceptedor rejected, respectively. In this example, the user has accepted mostof the suggested codes, but has rejected code 571.5 because the userbelieves the mention of “Alcoholism” in the documentation makes thediagnosis of “Cirrhosis of Liver w/o Alcohol” incorrect. Exemplary GUI700 further allows the user to provide a reason for the rejection of acode, such as by using the exemplary context menu illustrated in FIG.7F. In some embodiments, the reasons provided by users for rejectingparticular automatically suggested codes may be used for review and/ortraining purposes (e.g., for training the NLU engine, e.g., of the CLUsystem to derive more accurate codes from documentation text).

GUI 700 may also allow the user to replace a code with a different code,instead of rejecting the code outright, e.g., using the context menu ofFIG. 7C. In the example illustrated in FIG. 7E, the user has replacedcode 482.9 with code 482.1, and indicator 738 shows that the new codewas user-added. 482.9 (Pneumonia due to Pseudomonas) is a more specificdiagnosis applicable to the patient encounter than the suggested 482.1(Bacterial Pneumonia, Unspecified), so the user may provide “Morespecific code needed” as the reason for the replacement. In someembodiments, when a user replaces an automatically suggested code with adifferent code, any documentation text that was linked to the originallysuggested code may then be linked to the replacement code. Suchreplacement codes, optionally with linked text and/or replacementreasons, may also be used as feedback, e.g., for training of the CLUsystem.

The user can also add a code to the list for a patient encounter bymanually inputting the code in input field 740. For example, FIG. 7Eshows a new code 041.7 that has been added by the user. The user maylink the added code to supporting portion(s) of the text, such as themention of “pseudomonas” in the Discharge Summary, e.g., by using the“Link Text” procedure described above.

In some embodiments, as discussed above, the CAC interface may providethe user with interactive access to a coding-related knowledge baseincluding the government-authorized codebooks for the standardized codesets being used to code the patient encounter. In some embodiments, GUI700 may be configured to allow the user to call up an appropriatecodebook section as part of reviewing and potentially correcting anautomatically derived and suggested code in code list 730. The user maycall up the codebook section by selecting the code for which knowledgebase review is desired; for example, in some embodiments, the contextmenu of FIG. 7C may include (in addition to the options listed in theexample menu illustrated in FIG. 7C, or as a replacement for one or moreof the options listed in the example of FIG. 7C, or in any othersuitable configuration) a “Verify” option that calls up the appropriatecodebook section for a selected code in code list 730. It should beappreciated that any menu options illustrated or described are merelyexemplary, and that a context menu such as that illustrated anddescribed herein may include any suitable set of available options,whether in the CAC system as initially developed or by latercustomization.

FIG. 7G illustrates a view of exemplary GUI 700 in which the user hasselected the exemplary “Verify” option for code 482.9 (automaticallyderived by the NLU engine and suggested by the CLU system) in code list730. In response to the user's selection, panels 710 and 720 inexemplary GUI 700 have been replaced with panel 760, in which a portionof the official ICD-9-CM codebook is displayed, open to the 482 codesection containing selected code 482.9. In some embodiments, in responseto the user's selection of the code to be verified in code list 730, theposition of the selected code in the codebook may be indicated in panel760. In this example, the ICD-9-CM codebook in panel 760 may be scrolledto the portion containing selected code 482.9, and code 482.9 may behighlighted in the displayed codebook or visually distinguished in anyother suitable way (not shown in FIG. 7G) to indicate its position.

As illustrated in FIG. 7G, the codebook may be stored as a datastructure representing a tree of medical billing codes, such as ICD-9-CMcodes in this particular example. The particular code selected by theuser for review may be positioned as a node in the tree, as determinedby the structure and content of the code set maintained and distributedby an appropriate government agency or other standard-setting entity. Indisplaying the portion of the codebook containing the selected code inpanel 760, the node corresponding to the selected code may be displayedin the context of adjacent nodes in the tree set forth by the code set.In this example, by consulting the codebook for selected code 482.9, theuser can view and explore in panel 760 the other codes in the 482 family(i.e., the codes whose nodes are branches of the 482 node in the codeset tree), as well as adjacent code families 481 and 483, etc.

The codebook displayed in panel 760 may include listings of coding rulesin some embodiments, and thus by providing interactive access toappropriate codebook sections for selected codes, the CAC interface mayadvantageously allow the user to gain targeted coding instruction thatis immediately relevant to codes suggested or being considered for aparticular patient encounter currently being coded. For instance, thecodebook portion displayed in the example in FIG. 7G contains two“excludes” rules: one indicating that code 482.3 should not be assignedin the same patient encounter as code 481, and one indicating that code482.8 should not be assigned in the same patient encounter as any codebetween 484.1 and 484.8. In addition, this codebook portion includes two“use additional code” rules: one indicating that an encounter to whichcode 481 is assigned should also have a code to identify the infectiousorganism causing the patient's Lobar pneumonia, and one indicating thatan encounter to which code 482.8 is assigned should also have a code toidentify the bacteria causing the pneumonia. It should be appreciatedthat these are just particular examples, and other types of coding rulesare also possible within a codebook. In some embodiments, the CAC systemmay provide an alert within GUI 700 when code list 730 contains anycombination of codes that violate one or more applicable coding rules.

Alternatively or additionally, in some embodiments the CAC interface mayallow the user to access any other suitable types of coding rules,guidance, tips, references, etc., that are relevant to particular codes,whether the resources are internal or external to thegovernment-authorized codebook for the code set. These may be collectedand stored in connection with the CAC system from any suitablesource(s), such as one or more government agencies, one or morethird-party providers of such content, hired coding experts, healthcarepersonnel, users of the CAC system, etc. In some embodiments, theknowledge base of the CAC system may be updated periodically and/orwhenever new publications or other additions are available from any ofthe various sources.

For example, in FIG. 7G, Clinical Indicators for a particular code maybe accessed, if available for that code, by selecting icon 762 for anycode next to which it appears in panel 760 of exemplary GUI 700. TheClinical Indicators may include details regarding the medical diagnosiscorresponding to the diagnosis code. For example, selecting the icon 762next to code 482.1 may cause the display, in any suitable location inexemplary GUI 700, of the following Clinical Indicators for thePneumonia due to Pseudomonas diagnosis:

-   -   General Description:    -   Inflammation of the lungs with consolidation due to the        pseudomonas microorganisms, most frequently pseudomonas        aeruginosa, a gram-negative, oxidase-positive rod. This type of        pneumonia is often found in AIDS patients.    -   Symptoms:    -   Include cough, fever, chills, nausea, vomiting, malaise, and        myalgia.    -   Documentation:    -   Chest ex-ray shows consolidation in the lung. CBC reveals        leukocytosis. Sputum culture reveals pseudomonas species.    -   Treatment:    -   Appropriate antibiotics. Symptomatic therapy.

As another example, in FIG. 7G, Coding Clinics for a particular code maybe accessed, if available for that code, by selecting icon 764 for anycode next to which it appears in panel 760 of exemplary GUI 700. Theseare an example of third-party publications containing guidance articlesfor medical coders. In this example, selecting the icon 764 next to code482.1 may cause the display in panel 760 of any one or more CodingClinics® published by the American Hospital Association that arerelevant to code 482.1.

As yet another non-limiting example of interactive knowledge baseresources that may be provided in a CAC interface in some embodiments,in FIG. 7G, Tips for a particular code may be accessed, if available forthat code, by selecting icon 766 for any code next to which it appearsin panel 760 of exemplary GUI 700. These may be notes on suggestions,best practices, etc., for a particular code, which may be gathered fromany suitable source(s), such as particular items of interest from otherpublications, observances of healthcare or coding personnel, etc. In theexample of FIG. 7G, selecting the icon 766 next to code 482.1 may causethe display of the following Tips in any suitable location in exemplaryGUI 700:

-   -   Code 799.02, Hypoxemia, can be assigned as an additional        diagnosis code when present with pneumonia since this condition        is not inherent in pneumonia. Source: CC 2Q 2006    -   When the terms sepsis, severe sepsis, or SIRS are used with        underlying infections other than septicemia such as pneumonia,        cellulitis or a nonspecified UTI, code the systemic infection        first (i.e. 038.9, 995.91) followed by the localized infection.        Source: CC 4Q 2003    -   When a patient is admitted with respiratory failure and another        acute condition, (e.g., myocardial infarction, cerebrovascular        accident, aspiration pneumonia), the principal diagnosis will        not be the same in every situation. This applies whether the        other acute condition is a respiratory condition or        nonrespiratory condition. Selection of the principal diagnosis        will be dependent on the circumstances of admission. If both the        respiratory failure and the other acute condition are equally        responsible for occasioning the admission to the hospital, and        there are no chapter specific sequencing rules, the guideline        regarding two or more diagnosis that equally meet the definition        for principal diagnosis may be applied in these situations.        Source: ICD-9-CM Official Coding Guidelines    -   The diagnosis of pneumonia due to specific etiologies or        causative organisms is based on the physician's diagnostic        statement. When a physician has not specified the etiology or        causative organism of the pneumonia, seek physician confirmation        and documentation regarding the significance of clinical signs,        symptoms, positive culture results, lab and radiology findings,        or response to treatment before assigning a more specific        pneumonia diagnosis. Source: CC 1998 2Q

It should be appreciated that the foregoing are merely some examples,and embodiments are not limited to any or all of these. Any suitableother type(s) of knowledge base resources may also be accessed via theCAC system in some embodiments.

In some embodiments, the CAC interface may be configured to allow theuser to navigate the codebook to explore other codes once the codebookdisplay is opened in connection with an original code selected forreview from code list 730. Further, in some embodiments the CACinterface may be configured to allow the user to select a different codewithin the codebook to replace the original code in code list 730 forcoding the current patient encounter. In the example depicted in FIG.7G, the user has navigated from original code 482.9 to replacement code482.1, as indicated by the current highlighting of code 482.1. The usercan then select the code for replacement in any suitable way, such asright-clicking on code 482.1 and selecting “Replace” from a contextmenu. As in the other code replacement process described previouslyabove, in some embodiments GUI 700 may then provide an option for theuser to specify a reason for the replacement. This information (theuser's replacement of the original code with the replacement code,optionally accompanied by one or more reasons for the replacement) maylater be used for quality review purposes, for re-training the NLUengine performing the automatic suggestion of codes, etc. In someembodiments, information regarding which codes the user viewed withinthe codebook view and/or which additional resources were viewed mayalternatively or additionally be retained for quality review and/ortraining purposes.

In the example of FIG. 7G, when the user is finished with the codebookview, the user may select button 768 to exit the view and return todocument panels 710 and 720 in exemplary GUI 700. Alternatively oradditionally, the codebook view may close automatically in someembodiments in response to any suitable trigger, such as the user'scompletion of the process of replacing the code being researched withanother code from the codebook.

When the user has completed the review of the codes and supportingdocumentation, exemplary GUI 700 allows the user to submit the codes forfinalization by selecting button 750. FIG. 8 illustrates an exemplarycode finalization screen 800 that may be displayed following the user'sselection of submit button 750. In exemplary screen 800, all of theaccepted and user-added codes are displayed for final review.Alternatively, in some embodiments the user may be required toaffirmatively accept even user-added codes before they will appear incode finalization screen 800. The codes are displayed in screen 800 inan ordered sequence, which the user may change by re-ordering the codes.In some embodiments, the order of the finalized sequence of codes may beused in later processes such as billing, to determine the principaldiagnosis, etc. Exemplary screen 800 also includes fields for “presenton admission” (POA) indicators, which provide information on whethereach diagnosis was present when the patient was admitted to thehospital, or was acquired during the hospital stay. This information maybe required documentation in some circumstances, and in some embodimentsmay be used for review and/or training purposes. In some embodiments,POA indicators may be automatically suggested, e.g., using the CLUsystem; while in other embodiments, POA indicators may only be inputmanually.

When the user is satisfied with the finalized sequence of codes,exemplary screen 800 provides a button 810 for the codes to be saved, atwhich the coding process for the patient encounter becomes complete. Insome embodiments, the CAC system may compare the finalized sequence ofcodes with stored coding rules, and may present the user with anyapplicable error or warning notifications prior to saving. As discussedabove, once saved, the finalized sequence of codes may be sent to otherprocesses such as billing and quality review, and in some embodimentsmay be used for performance review and/or training of the CLU and/or CACsystems.

Like the embodiments of the CLU system 100 described above, the CACsystem in accordance with the techniques described herein may take anysuitable form, as embodiments are not limited in this respect. Anillustrative implementation of a computer system 900 that may be used inconnection with some implementations of a CAC system is shown in FIG. 9.One or more computer systems such as computer system 900 may be used toimplement any of the functionality of the CAC system described above. Asshown, the computer system 900 may include one or more processors 910and one or more tangible, non-transitory computer-readable storage media(e.g., volatile storage 920 and one or more non-volatile storage media930, which may be formed of any suitable non-volatile data storagemedia). The processor 910 may control writing data to and reading datafrom the volatile storage 920 and the non-volatile storage media 930 inany suitable manner, as the aspects of the present invention are notlimited in this respect. To perform any of the functionality describedherein, the processor 910 may execute one or more instructions stored inone or more computer-readable storage media (e.g., volatile storage920), which may serve as tangible, non-transitory computer-readablestorage media storing instructions for execution by the processor 910.

The above-described embodiments of the present invention can beimplemented in any of numerous ways. For example, the embodiments may beimplemented using hardware, software or a combination thereof. Whenimplemented in software, the software code can be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers. It should beappreciated that any component or collection of components that performthe functions described above can be generically considered as one ormore controllers that control the above-discussed functions. The one ormore controllers can be implemented in numerous ways, such as withdedicated hardware, or with general purpose hardware (e.g., one or moreprocessors) that is programmed using microcode or software to performthe functions recited above.

In this respect, it should be appreciated that one implementation ofembodiments of the present invention comprises at least onecomputer-readable storage medium (i.e., a tangible, non-transitorycomputer-readable medium, such as a computer memory, a floppy disk, acompact disk, a magnetic tape, or other tangible, non-transitorycomputer-readable medium) encoded with a computer program (i.e., aplurality of instructions), which, when executed on one or moreprocessors, performs above-discussed functions of embodiments of thepresent invention. The computer-readable storage medium can betransportable such that the program stored thereon can be loaded ontoany computer resource to implement aspects of the present inventiondiscussed herein. In addition, it should be appreciated that thereference to a computer program which, when executed, performs any ofthe above-discussed functions, is not limited to an application programrunning on a host computer. Rather, the term “computer program” is usedherein in a generic sense to reference any type of computer code (e.g.,software or microcode) that can be employed to program one or moreprocessors to implement above-discussed aspects of the presentinvention.

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing”, “involving”, andvariations thereof, is meant to encompass the items listed thereafterand additional items. Use of ordinal terms such as “first,” “second,”“third,” etc., in the claims to modify a claim element does not byitself connote any priority, precedence, or order of one claim elementover another or the temporal order in which acts of a method areperformed. Ordinal terms are used merely as labels to distinguish oneclaim element having a certain name from another element having a samename (but for use of the ordinal term), to distinguish the claimelements from each other.

Having described several embodiments of the invention in detail, variousmodifications and improvements will readily occur to those skilled inthe art. Such modifications and improvements are intended to be withinthe spirit and scope of the invention. Accordingly, the foregoingdescription is by way of example only, and is not intended as limiting.The invention is limited only as defined by the following claims and theequivalents thereto.

What is claimed is:
 1. A method comprising: applying a natural languageunderstanding engine to a free-form text documenting at least oneclinical patient encounter to generate a set of one or more medicalbilling codes for the at least one clinical patient encounter; inresponse to a user's selection of a first medical billing code of thegenerated set of medical billing codes in a user interface configured toallow one or more human users to review and correct the generated set ofmedical billing codes, the user interface comprising a window having afirst portion and a second portion, the first medical billing code beinga member of a standardized code set and the generated set of medicalbilling codes being displayed within the first portion of the window inthe user interface: displaying within the second portion of the windowin the user interface, and simultaneously with displaying the generatedset of medical billing codes within the first portion of the window inthe user interface, at least a contiguous portion of a codebookspecifying an order and hierarchy of codes in the standardized code set,the at least a contiguous portion of the codebook including at least thefirst medical billing code and a second medical billing code, andindicating a position of the first medical billing code within the orderand hierarchy of codes in the standardized code set specified in thedisplayed at least a contiguous portion of the codebook, whereindisplaying the at least a contiguous portion of the codebook comprisesmaintaining the order and hierarchy of the codes in the at least acontiguous portion of the codebook as displayed in the second portion ofthe window in the user interface in response to the user's selection ofthe first medical billing code in the first portion of the window in theuser interface; receiving a user's selection of the second medicalbilling code within the second portion of the window in the userinterface; and in response to the user's selection of the second medicalbilling code within the second portion of the window in the userinterface, replacing the first medical billing code in the first portionof the window in the user interface with the second medical billing codeselected in the second portion of the window in the user interface,wherein the receiving and the replacing are performed while the firstportion and the second portion are simultaneously displayed within thewindow.
 2. The method of claim 1, wherein the codebook is an officialICD or HCPCS codebook.
 3. The method of claim 1, wherein the codebookcomprises a data structure representing a tree of medical billing codes,wherein the position of the first medical billing code selected by theuser is a node in the tree.
 4. The method of claim 3, wherein displayingthe at least the contiguous portion of the codebook comprises displayingthe node corresponding to the first medical billing code selected by theuser in context of adjacent nodes in the tree.
 5. The method of claim 1,further comprising displaying, within the user interface, one or morecoding rules for the first medical billing code selected by the user. 6.The method of claim 1, further comprising allowing the user to navigatethe at least a contiguous portion of the codebook to select the secondmedical billing code as a replacement for the first medical billingcode.
 7. The method of claim 6, further comprising re-training thenatural language understanding engine based at least in part on theuser's selection of the second medical billing code as the replacementfor the first medical billing code.
 8. The method of claim 1, furthercomprising: determining whether a combination of medical billing codesin the generated set of medical billing codes violates one or morecoding rules associated with the first medical billing code; andgenerating an alert in response to determining that the combination ofmedical billing codes violates the one or more coding rules associatedwith the first medical billing code.
 9. The method of claim 8, whereinthe one or more coding rules comprises a first rule that indicates thata particular medical billing code is not to be assigned in the same atleast one clinical patient encounter as the first medical billing codeor a second rule that indicates that an additional medical billing codeis to be assigned in the same at least one clinical patient encounter asthe first medical billing code.
 10. The method of claim 1, furthercomprising: displaying at least a first and a second user-selectablegraphical user interface (GUI) element associated with the first medicalbilling code within the second portion of the window in the userinterface; in response to a selection of the first user-selectable GUIelement, displaying information relating to guidance for the firstmedical billing code; and in response to a selection of the seconduser-selectable GUI element, displaying information relating to tips forthe first medical billing code.
 11. The method of claim 1, furthercomprising: scrolling to at least the contiguous portion of the codebookcontaining the first medical billing code, and visually distinguishingthe first medical billing code from other codes displayed in the atleast the contiguous portion of the codebook.
 12. At least onenon-transitory computer-readable storage medium storingcomputer-executable instructions that, when executed, perform a methodcomprising: applying a natural language understanding engine to afree-form text documenting at least one clinical patient encounter togenerate a set of one or more medical billing codes for the at least oneclinical patient encounter; in response to a user's selection of a firstmedical billing code of the generated set of medical billing codes in auser interface configured to allow one or more human users to review andcorrect the generated set of medical billing codes, the user interfacecomprising a window having a first portion and a second portion, thefirst medical billing code being a member of a standardized code set andthe generated set of medical billing codes being displayed within thefirst portion of the window in the user interface: displaying within thesecond portion of the window in the user interface, and simultaneouslywith displaying the generated set of medical billing codes within thefirst portion of the window in the user interface, at least a contiguousportion of a codebook specifying an order and hierarchy of codes in thestandardized code set, the at least a contiguous portion of the codebookincluding at least the first medical billing code and a second medicalbilling code, and indicating a position of the first medical billingcode within the order and hierarchy of codes in the standardized codeset specified in the displayed at least a contiguous portion of thecodebook, wherein displaying the at least a contiguous portion of thecodebook comprises maintaining the order and hierarchy of the codes inthe at least a contiguous portion of the codebook as displayed in thesecond portion of the window in the user interface in response to theuser's selection of the first medical billing code in the first portionof the window in the user interface; receiving a user's selection of thesecond medical billing code within the second portion of the window inthe user interface; and in response to the user's selection of thesecond medical billing code within the second portion of the window inthe user interface, replacing the first medical billing code in thefirst portion of the window in the user interface with the secondmedical billing code selected in the second portion of the window in theuser interface, wherein the receiving and the replacing are performedwhile the first portion and the second portion are simultaneouslydisplayed within the window.
 13. The at least one non-transitorycomputer-readable storage medium of claim 12, wherein the codebookcomprises a data structure representing a tree of medical billing codes,and wherein the position of the first medical billing code selected bythe user is a node in the tree.
 14. The at least one non-transitorycomputer-readable storage medium of claim 13, wherein displaying the atleast the contiguous portion of the codebook comprises displaying thenode corresponding to the first medical billing code selected by theuser in context of adjacent nodes in the tree.
 15. The at least onenon-transitory computer-readable storage medium of claim 12, wherein themethod further comprises displaying, within the user interface, one ormore coding rules for the first medical billing code selected by theuser.
 16. The at least one non-transitory computer-readable storagemedium of claim 12, wherein the method further comprises allowing theuser to navigate the at least a contiguous portion of the codebook toselect the second medical billing code as a replacement for the firstmedical billing code.
 17. The at least one non-transitorycomputer-readable storage medium of claim 16, wherein the method furthercomprises re-training the natural language understanding engine based atleast in part on the user's selection of the second medical billing codeas the replacement for the first medical billing code.
 18. Apparatuscomprising: at least one processor; and at least one computer-readablestorage medium storing processor-executable instructions that, whenexecuted by the at least one processor, perform a method comprising:applying a natural language understanding engine to a free-form textdocumenting at least one clinical patient encounter to generate a set ofone or more medical billing codes for the at least one clinical patientencounter; in response to a user's selection of a first medical billingcode of the generated set of medical billing codes in a user interfaceconfigured to allow one or more human users to review and correct thegenerated set of medical billing codes, the user interface comprising awindow having a first portion and a second portion, the first medicalbilling code being a member of a standardized code set and the generatedset of medical billing codes being displayed within the first portion ofthe window in the user interface: displaying within the second portionof the window in the user interface, and simultaneously with displayingthe generated set of medical billing codes within the first portion ofthe window in the user interface, at least a contiguous portion of acodebook specifying an order and hierarchy of codes in the standardizedcode set, the at least a contiguous portion of the codebook including atleast the first medical billing code and a second medical billing code,and indicating a position of the first medical billing code within theorder and hierarchy of codes in the standardized code set specified inthe displayed at least a contiguous portion of the codebook, whereindisplaying the at least a contiguous portion of the codebook comprisesmaintaining the order and hierarchy of the codes in the at least acontiguous portion of the codebook as displayed in the second portion ofthe window in the user interface in response to the user's selection ofthe first medical billing code in the first portion of the window in theuser interface; receiving a user's selection of the second medicalbilling code within the second portion of the window in the userinterface; and in response the user's selection of the second medicalbilling code within the second portion of the window in the userinterface, replacing the first medical billing code in the first portionof the window in the user interface with the second medical billing codeselected in the second portion of the window in the user interface,wherein the receiving and the replacing are performed while the firstportion and the second portion are simultaneously displayed within thewindow.
 19. The apparatus of claim 18, wherein the codebook comprises adata structure representing a tree of medical billing codes, wherein theposition of the first medical billing code selected by the user is anode in the tree.
 20. The apparatus of claim 19, wherein displaying theat least the contiguous portion of the codebook comprises displaying thenode corresponding to the first medical billing code selected by theuser in context of adjacent nodes in the tree.